Apparatus, system and method of radar antenna calibration

ABSTRACT

For example, a radar apparatus may include an input to receive radar receive (Rx) data, the radar Rx data based on radar signals received via a plurality of Rx antennas of Multiple-Input-Multiple-Output (MIMO) radar antenna; and a radar processor configured to generate radar information based on the radar Rx data by calibrating an antenna Mismatch (MM) of the MIMO radar antenna such that the radar information includes an Angle of Arrival (AoA) spectrum having a Peak Side Lobe Level (PSLL) of at least 30 decibel (dB).

CROSS REFERENCE

This application claims the benefit of, and priority from, U.S.Provisional Patent Application No. 63/005,773 entitled “APPARATUS,SYSTEM AND METHOD OF RADAR ANTENNA CALIBRATION”, filed Apr. 6, 2020, theentire disclosure of which is incorporated herein by reference.

TECHNICAL FIELD

Aspects described herein generally relate to radar antenna calibration.

BACKGROUND

Multiple Input Multiple Output (MIMO) radar is a technology that allowsreduction of a physical array aperture and a number of antenna elementsby transmission of orthogonal signals from a transmit (Tx) array with aplurality of elements, and processing received signals via a receive(Rx) array with a plurality of elements.

As antennas are manufactured with some enhanced phase and gain as theirground state, an antenna array may be dysfunctional without a propercalibration of antenna elements.

BRIEF DESCRIPTION OF THE DRAWINGS

For simplicity and clarity of illustration, elements shown in thefigures have not necessarily been drawn to scale. For example, thedimensions of some of the elements may be exaggerated relative to otherelements for clarity of presentation. Furthermore, reference numeralsmay be repeated among the figures to indicate corresponding or analogouselements. The figures are listed below.

FIG. 1 is a schematic block diagram illustration of a vehicleimplementing a radar, in accordance with some demonstrative aspects.

FIG. 2 is a schematic block diagram illustration of a robot implementinga radar, in accordance with some demonstrative aspects.

FIG. 3 is a schematic block diagram illustration of a radar apparatus,in accordance with some demonstrative aspects.

FIG. 4 is a schematic block diagram illustration of aFrequency-Modulated Continuous Wave (FMCW) radar apparatus, inaccordance with some demonstrative aspects.

FIG. 5 is a schematic illustration of an extraction scheme, which may beimplemented to extract range and speed (Doppler) estimations fromdigital reception radar data values, in accordance with somedemonstrative aspects.

FIG. 6 is a schematic illustration of an angle-determination scheme,which may be implemented to determine Angle of Arrival (AoA) informationbased on an incoming radio signal received by a receive antenna array,in accordance with some demonstrative aspects.

FIG. 7 is a schematic illustration of a Multiple-Input-Multiple-Output(MIMO) radar antenna scheme, which may be implemented based on acombination of Transmit (Tx) and Receive (Rx) antennas, in accordancewith some demonstrative aspects.

FIG. 8 is a schematic block diagram illustration of a radar frontend anda radar processor, in accordance with some demonstrative aspects.

FIG. 9 is a schematic illustration of a radar detection scenario, and agraph depicting a plurality of AoA spectrum images corresponding to theradar detection scenario, to demonstrate a technical problem, which maybe addressed in accordance with some demonstrative aspects.

FIG. 10 is a schematic illustration of performance graphs of radarprocessing with antenna mismatch calibration, in accordance with somedemonstrative aspects.

FIG. 11A is a schematic illustration of a graph depicting a signal toNoise Ratio (SNR) performance of radar processing with antenna mismatchcalibration, in accordance with some demonstrative aspects.

FIG. 11B is a schematic illustration of a graph depicting a Peak SideLobe Level (PSLL) performance of radar processing with antenna mismatchcalibration, in accordance with some demonstrative aspects.

FIG. 12A is a schematic illustration of a graph depicting an SNR lossperformance of radar processing with antenna mismatch calibration, inaccordance with some demonstrative aspects.

FIG. 12B is a schematic illustration of a graph depicting a PSLLperformance of radar processing with antenna mismatch calibration, inaccordance with some demonstrative aspects.

FIG. 13 is a schematic illustration of graphs depicting antenna gainmismatches, which may be used as a reference in accordance with somedemonstrative aspects.

FIG. 14 is a schematic flow-chart illustration of a method of radarantenna calibration, in accordance with some demonstrative aspects.

FIG. 15 is a schematic illustration of a product of manufacture, inaccordance with some demonstrative aspects.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of some aspects.However, it will be understood by persons of ordinary skill in the artthat some aspects may be practiced without these specific details. Inother instances, well-known methods, procedures, components, unitsand/or circuits have not been described in detail so as not to obscurethe discussion.

Discussions herein utilizing terms such as, for example, “processing”,“computing”, “calculating”, “determining”, “establishing”, “analyzing”,“checking”, or the like, may refer to operation(s) and/or process(es) ofa computer, a computing platform, a computing system, or otherelectronic computing device, that manipulate and/or transform datarepresented as physical (e.g., electronic) quantities within thecomputer's registers and/or memories into other data similarlyrepresented as physical quantities within the computer's registersand/or memories or other information storage medium that may storeinstructions to perform operations and/or processes.

The terms “plurality” and “a plurality”, as used herein, include, forexample, “multiple” or “two or more”. For example, “a plurality ofitems” includes two or more items.

The words “exemplary” and “demonstrative” are used herein to mean“serving as an example, instance, demonstration, or illustration”. Anyaspect, aspect, or design described herein as “exemplary” or“demonstrative” is not necessarily to be construed as preferred oradvantageous over other aspects, aspects, or designs.

References to “one aspect”, “an aspect”, “demonstrative aspect”,“various aspects” “one aspect”, “an aspect”, “demonstrative aspect”,“various aspects” etc., indicate that the aspect(s) and/or aspects sodescribed may include a particular feature, structure, orcharacteristic, but not every aspect or aspect necessarily includes theparticular feature, structure, or characteristic. Further, repeated useof the phrase “in one aspect” or “in one aspect” does not necessarilyrefer to the same aspect or aspect, although it may.

As used herein, unless otherwise specified the use of the ordinaladjectives “first”, “second”, “third” etc., to describe a common object,merely indicate that different instances of like objects are beingreferred to, and are not intended to imply that the objects so describedmust be in a given sequence, either temporally, spatially, in ranking,or in any other manner.

The phrases “at least one” and “one or more” may be understood toinclude a numerical quantity greater than or equal to one, e.g., one,two, three, four, [ . . . ], etc. The phrase “at least one of” withregard to a group of elements may be used herein to mean at least oneelement from the group consisting of the elements. For example, thephrase “at least one of” with regard to a group of elements may be usedherein to mean one of the listed elements, a plurality of one of thelisted elements, a plurality of individual listed elements, or aplurality of a multiple of individual listed elements.

The term “data” as used herein may be understood to include informationin any suitable analog or digital form, e.g., provided as a file, aportion of a file, a set of files, a signal or stream, a portion of asignal or stream, a set of signals or streams, and the like. Further,the term “data” may also be used to mean a reference to information,e.g., in form of a pointer. The term “data”, however, is not limited tothe aforementioned examples and may take various forms and/or mayrepresent any information as understood in the art.

The terms “processor” or “controller” may be understood to include anykind of technological entity that allows handling of any suitable typeof data and/or information. The data and/or information may be handledaccording to one or more specific functions executed by the processor orcontroller. Further, a processor or a controller may be understood asany kind of circuit, e.g., any kind of analog or digital circuit. Aprocessor or a controller may thus be or include an analog circuit,digital circuit, mixed-signal circuit, logic circuit, processor,microprocessor, Central Processing Unit (CPU), Graphics Processing Unit(GPU), Digital Signal Processor (DSP), Field Programmable Gate Array(FPGA), integrated circuit, Application Specific Integrated Circuit(ASIC), and the like, or any combination thereof. Any other kind ofimplementation of the respective functions, which will be describedbelow in further detail, may also be understood as a processor,controller, or logic circuit. It is understood that any two (or more)processors, controllers, or logic circuits detailed herein may berealized as a single entity with equivalent functionality or the like,and conversely that any single processor, controller, or logic circuitdetailed herein may be realized as two (or more) separate entities withequivalent functionality or the like.

The term “memory” is understood as a computer-readable medium (e.g., anon-transitory computer-readable medium) in which data or informationcan be stored for retrieval. References to “memory” may thus beunderstood as referring to volatile or non-volatile memory, includingrandom access memory (RAM), read-only memory (ROM), flash memory,solid-state storage, magnetic tape, hard disk drive, optical drive,among others, or any combination thereof. Registers, shift registers,processor registers, data buffers, among others, are also embracedherein by the term memory. The term “software” may be used to refer toany type of executable instruction and/or logic, including firmware.

A “vehicle” may be understood to include any type of driven object. Byway of example, a vehicle may be a driven object with a combustionengine, an electric engine, a reaction engine, an electrically drivenobject, a hybrid driven object, or a combination thereof. A vehicle maybe, or may include, an automobile, a bus, a mini bus, a van, a truck, amobile home, a vehicle trailer, a motorcycle, a bicycle, a tricycle, atrain locomotive, a train wagon, a moving robot, a personal transporter,a boat, a ship, a submersible, a submarine, a drone, an aircraft, arocket, among others.

A “ground vehicle” may be understood to include any type of vehicle,which is configured to traverse the ground, e.g., on a street, on aroad, on a track, on one or more rails, off-road, or the like.

An “autonomous vehicle” may describe a vehicle capable of implementingat least one navigational change without driver input. A navigationalchange may describe or include a change in one or more of steering,braking, acceleration/deceleration, or any other operation relating tomovement, of the vehicle. A vehicle may be described as autonomous evenin case the vehicle is not fully autonomous, for example, fullyoperational with driver or without driver input. Autonomous vehicles mayinclude those vehicles that can operate under driver control duringcertain time periods, and without driver control during other timeperiods. Additionally or alternatively, autonomous vehicles may includevehicles that control only some aspects of vehicle navigation, such assteering, e.g., to maintain a vehicle course between vehicle laneconstraints, or some steering operations under certain circumstances,e.g., not under all circumstances, but may leave other aspects ofvehicle navigation to the driver, e.g., braking or braking under certaincircumstances. Additionally or alternatively, autonomous vehicles mayinclude vehicles that share the control of one or more aspects ofvehicle navigation under certain circumstances, e.g., hands-on, such asresponsive to a driver input; and/or vehicles that control one or moreaspects of vehicle navigation under certain circumstances, e.g.,hands-off, such as independent of driver input. Additionally oralternatively, autonomous vehicles may include vehicles that control oneor more aspects of vehicle navigation under certain circumstances, suchas under certain environmental conditions, e.g., spatial areas, roadwayconditions, or the like. In some aspects, autonomous vehicles may handlesome or all aspects of braking, speed control, velocity control,steering, and/or any other additional operations, of the vehicle. Anautonomous vehicle may include those vehicles that can operate without adriver. The level of autonomy of a vehicle may be described ordetermined by the Society of Automotive Engineers (SAE) level of thevehicle, e.g., as defined by the SAE, for example in SAE J3016 2018:Taxonomy and definitions for terms related to driving automation systemsfor on road motor vehicles, or by other relevant professionalorganizations. The SAE level may have a value ranging from a minimumlevel, e.g., level 0 (illustratively, substantially no drivingautomation), to a maximum level, e.g., level 5 (illustratively, fulldriving automation).

The phrase “vehicle operation data” may be understood to describe anytype of feature related to the operation of a vehicle. By way ofexample, “vehicle operation data” may describe the status of thevehicle, such as, the type of tires of the vehicle, the type of vehicle,and/or the age of the manufacturing of the vehicle. More generally,“vehicle operation data” may describe or include static features orstatic vehicle operation data (illustratively, features or data notchanging over time). As another example, additionally or alternatively,“vehicle operation data” may describe or include features changingduring the operation of the vehicle, for example, environmentalconditions, such as weather conditions or road conditions during theoperation of the vehicle, fuel levels, fluid levels, operationalparameters of the driving source of the vehicle, or the like. Moregenerally, “vehicle operation data” may describe or include varyingfeatures or varying vehicle operation data (illustratively, time varyingfeatures or data).

Some aspects may be used in conjunction with various devices andsystems, for example, a radar sensor, a radar device, a radar system, avehicle, a vehicular system, an autonomous vehicular system, a vehicularcommunication system, a vehicular device, an airborne platform, awaterborne platform, road infrastructure, sports-capture infrastructure,city monitoring infrastructure, static infrastructure platforms, indoorplatforms, moving platforms, robot platforms, industrial platforms, asensor device, a User Equipment (UE), a Mobile Device (MD), a wirelessstation (STA), a sensor device, a non-vehicular device, a mobile orportable device, and the like.

Some aspects may be used in conjunction with Radio Frequency (RF)systems, radar systems, vehicular radar systems, autonomous systems,robotic systems, detection systems, or the like.

Some demonstrative aspects may be used in conjunction with an RFfrequency in a frequency band having a starting frequency above 10Gigahertz (GHz), for example, a frequency band having a startingfrequency between 10 GHz and 120 GHz. For example, some demonstrativeaspects may be used in conjunction with an RF frequency having astarting frequency above 30 GHz, for example, above 45 GHz, e.g., above60 GHz. For example, some demonstrative aspects may be used inconjunction with an automotive radar frequency band, e.g., a frequencyband between 76 GHz and 81 GHz. However, other aspects may beimplemented utilizing any other suitable frequency bands, for example, afrequency band above 140 GHz, a frequency band of 300 GHz, a subTerahertz (THz) band, a THz band, an Infra Red (IR) band, and/or anyother frequency band.

As used herein, the term “circuitry” may refer to, be part of, orinclude, an Application Specific Integrated Circuit (ASIC), anintegrated circuit, an electronic circuit, a processor (shared,dedicated, or group), and/or memory (shared, dedicated, or group), thatexecute one or more software or firmware programs, a combinational logiccircuit, and/or other suitable hardware components that provide thedescribed functionality. In some aspects, the circuitry may beimplemented in, or functions associated with the circuitry may beimplemented by, one or more software or firmware modules. In someaspects, circuitry may include logic, at least partially operable inhardware.

The term “logic” may refer, for example, to computing logic embedded incircuitry of a computing apparatus and/or computing logic stored in amemory of a computing apparatus. For example, the logic may beaccessible by a processor of the computing apparatus to execute thecomputing logic to perform computing functions and/or operations. In oneexample, logic may be embedded in various types of memory and/orfirmware, e.g., silicon blocks of various chips and/or processors. Logicmay be included in, and/or implemented as part of, various circuitry,e.g., radio circuitry, receiver circuitry, control circuitry,transmitter circuitry, transceiver circuitry, processor circuitry,and/or the like. In one example, logic may be embedded in volatilememory and/or non-volatile memory, including random access memory, readonly memory, programmable memory, magnetic memory, flash memory,persistent memory, and/or the like. Logic may be executed by one or moreprocessors using memory, e.g., registers, buffers, stacks, and the like,coupled to the one or more processors, e.g., as necessary to execute thelogic.

The term “communicating” as used herein with respect to a signalincludes transmitting the signal and/or receiving the signal. Forexample, an apparatus, which is capable of communicating a signal, mayinclude a transmitter to transmit the signal, and/or a receiver toreceive the signal. The verb communicating may be used to refer to theaction of transmitting or the action of receiving. In one example, thephrase “communicating a signal” may refer to the action of transmittingthe signal by a transmitter, and may not necessarily include the actionof receiving the signal by a receiver. In another example, the phrase“communicating a signal” may refer to the action of receiving the signalby a receiver, and may not necessarily include the action oftransmitting the signal by a transmitter.

The term “antenna”, as used herein, may include any suitableconfiguration, structure and/or arrangement of one or more antennaelements, components, units, assemblies and/or arrays. In some aspects,the antenna may implement transmit and receive functionalities usingseparate transmit and receive antenna elements. In some aspects, theantenna may implement transmit and receive functionalities using commonand/or integrated transmit/receive elements. The antenna may include,for example, a phased array antenna, a single element antenna, a set ofswitched beam antennas, and/or the like. In one example, an antenna maybe implemented as a separate element or an integrated element, forexample, as an on-module antenna, an on-chip antenna, or according toany other antenna architecture.

Some demonstrative aspects are described herein with respect to RF radarsignals. However, other aspects may be implemented with respect to, orin conjunction with, any other radar signals, wireless signals, IRsignals, acoustic signals, optical signals, wireless communicationsignals, communication scheme, network, standard, and/or protocol. Forexample, some demonstrative aspects may be implemented with respect tosystems, e.g., Light Detection Ranging (LiDAR) systems, and/or sonarsystems, utilizing light and/or acoustic signals.

Reference is now made to FIG. 1 , which schematically illustrates ablock diagram of a vehicle 100 implementing a radar, in accordance withsome demonstrative aspects.

In some demonstrative aspects, vehicle 100 may include a car, a truck, amotorcycle, a bus, a train, an airborne vehicle, a waterborne vehicle, acart, a golf cart, an electric cart, a road agent, or any other vehicle.

In some demonstrative aspects, vehicle 100 may include a radar device101, e.g., as described below. For example, radar device 101 may includea radar detecting device, a radar sensing device, a radar sensor, or thelike, e.g., as described below.

In some demonstrative aspects, radar device 101 may be implemented aspart of a vehicular system, for example, a system to be implementedand/or mounted in vehicle 100.

In one example, radar device 101 may be implemented as part of anautonomous vehicle system, an automated driving system, a driverassistance and/or support system, and/or the like.

For example, radar device 101 may be installed in vehicle 101 fordetection of nearby objects, e.g., for autonomous driving.

In some demonstrative aspects, radar device 101 may be configured todetect targets in a vicinity of vehicle 100, e.g., in a far vicinityand/or a near vicinity, for example, using RF and analog chains,capacitor structures, large spiral transformers and/or any otherelectronic or electrical elements, e.g., as described below. In oneexample, radar device 101 may be mounted onto, placed, e.g., directly,onto, or attached to, vehicle 100.

In some demonstrative aspects, vehicle 100 may include a single radardevice 101. In other aspects, vehicle 100 may include a plurality ofradar devices 101, for example, at a plurality of locations, e.g.,around vehicle 100.

In some demonstrative aspects, radar device 101 may be implemented as acomponent in a suite of sensors used for driver assistance and/orautonomous vehicles, for example, due to the ability of radar to operatein nearly all-weather conditions.

In some demonstrative aspects, radar device 101 may be configured tosupport autonomous vehicle usage, e.g., as described below.

In one example, radar device 101 may determine a class, a location, anorientation, a velocity, an intention, a perceptional understanding ofthe environment, and/or any other information corresponding to an objectin the environment.

In another example, radar device 101 may be configured to determine oneor more parameters and/or information for one or more operations and/ortasks, e.g., path planning, and/or any other tasks.

In some demonstrative aspects, radar device 101 may be configured to mapa scene by measuring targets' echoes (reflectivity) and discriminatingthem, for example, mainly in range, velocity, azimuth and/or elevation,e.g., as described below.

In some demonstrative aspects, radar device 101 may be configured todetect, and/or sense, one or more objects, which are located in avicinity, e.g., a far vicinity and/or a near vicinity, of the vehicle100, and to provide one or more parameters, attributes, and/orinformation with respect to the objects.

In some demonstrative aspects, the objects may include other vehicles;pedestrians; traffic signs; traffic lights; roads, road elements, e.g.,a pavement-road meeting, an edge line; a hazard, e.g., a tire, a box, acrack in the road surface; and/or the like.

In some demonstrative aspects, the one or more parameters, attributesand/or information with respect to the object may include a range of theobjects from the vehicle 100, an angle of the object with respect to thevehicle 100, a location of the object with respect to the vehicle 100, arelative speed of the object with respect to vehicle 100, and/or thelike.

In some demonstrative aspects, radar device 101 may include a MultipleInput Multiple Output (MIMO) radar device 101, e.g., as described below.In one example, the MIMO radar device may be configured to utilize“spatial filtering” processing, for example, beamforming and/or anyother mechanism, for one or both of Transmit (Tx) signals and/or Receive(Rx) signals.

Some demonstrative aspects are described below with respect to a radardevice, e.g., radar device 101, implemented as a MIMO radar. However, inother aspects, radar device 101 may be implemented as any other type ofradar utilizing a plurality of antenna elements, e.g., a Single InputMultiple Output (SIMO) radar or a Multiple Input Single output (MISO)radar.

Some demonstrative aspects may be implemented with respect to a radardevice, e.g., radar device 101, implemented as a MIMO radar, e.g., asdescribed below. However, in other aspects, radar device 101 may beimplemented as any other type of radar, for example, an Electronic BeamSteering radar, a Synthetic Aperture Radar (SAR), adaptive and/orcognitive radars that change their transmission according to theenvironment and/or ego state, a reflect array radar, or the like.

In some demonstrative aspects, radar device 101 may include an antennaarrangement 102, a radar frontend 103 configured to communicate radarsignals via the antenna arrangement 102, and a radar processor 104configured to generate radar information based on the radar signals,e.g., as described below.

In some demonstrative aspects, radar processor 104 may be configured toprocess radar information of radar device 101 and/or to control one ormore operations of radar device 101, e.g., as described below.

In some demonstrative aspects, radar processor 104 may include, or maybe implemented, partially or entirely, by circuitry and/or logic, e.g.,one or more processors including circuitry and/or logic, memorycircuitry and/or logic.

Additionally or alternatively, one or more functionalities of radarprocessor 104 may be implemented by logic, which may be executed by amachine and/or one or more processors, e.g., as described below.

In one example, radar processor 104 may include at least one memory,e.g., coupled to the one or more processors, which may be configured,for example, to store, e.g., at least temporarily, at least some of theinformation processed by the one or more processors and/or circuitry,and/or which may be configured to store logic to be utilized by theprocessors and/or circuitry.

In other aspects, radar processor 104 may be implemented by one or moreadditional or alternative elements of vehicle 100.

In some demonstrative aspects, radar frontend 103 may include, forexample, one or more (radar) transmitters, and a one or more (radar)receivers, e.g., as described below.

In some demonstrative aspects, antenna arrangement 102 may include aplurality of antennas to communicate the radar signals. For example,antenna arrangement 102 may include multiple transmit antennas in theform of a transmit antenna array, and multiple receive antennas in theform of a receive antenna array. In another example, antenna arrangement102 may include one or more antennas used both as transmit and receiveantennas. In the latter case, the radar frontend 103, for example, mayinclude a duplexer, e.g., a circuit to separate transmitted signals fromreceived signals.

In some demonstrative aspects, as shown in FIG. 1 , the radar frontend103 and the antenna arrangement 102 may be controlled, e.g., by radarprocessor 104, to transmit a radio transmit signal 105.

In some demonstrative aspects, as shown in FIG. 1 , the radio transmitsignal 105 may be reflected by an object 106, resulting in an echo 107.

In some demonstrative aspects, the radar device 101 may receive the echo107, e.g., via antenna arrangement 102 and radar frontend 103, and radarprocessor 104 may generate radar information, for example, bycalculating information about position, radial velocity (Doppler),and/or direction of the object 106, e.g., with respect to vehicle 100.

In some demonstrative aspects, radar processor 104 may be configured toprovide the radar information to a vehicle controller 108 of the vehicle100, e.g., for autonomous driving of the vehicle 100.

In some demonstrative aspects, at least part of the functionality ofradar processor 104 may be implemented as part of vehicle controller108. In other aspects, the functionality of radar processor 104 may beimplemented as part of any other element of radar device 101 and/orvehicle 100. In other aspects, radar processor 104 may be implemented,as a separate part of, or as part of any other element of radar device101 and/or vehicle 100.

In some demonstrative aspects, vehicle controller 108 may be configuredto control one or more functionalities, modes of operation, components,devices, systems and/or elements of vehicle 100.

In some demonstrative aspects, vehicle controller 108 may be configuredto control one or more vehicular systems of vehicle 100, e.g., asdescribed below.

In some demonstrative aspects, the vehicular systems may include, forexample, a steering system, a braking system, a driving system, and/orany other system of the vehicle 100.

In some demonstrative aspects, vehicle controller 108 may configured tocontrol radar device 101, and/or to process one or parameters,attributes and/or information from radar device 101.

In some demonstrative aspects, vehicle controller 108 may be configured,for example, to control the vehicular systems of the vehicle 100, forexample, based on radar information from radar device 101 and/or one ormore other sensors of the vehicle 100, e.g., Light Detection and Ranging(LIDAR) sensors, camera sensors, and/or the like.

In one example, vehicle controller 108 may control the steering system,the braking system, and/or any other vehicular systems of vehicle 100,for example, based on the information from radar device 101, e.g., basedon one or more objects detected by radar device 101.

In other aspects, vehicle controller 108 may be configured to controlany other additional or alternative functionalities of vehicle 100.

Some demonstrative aspects are described herein with respect to a radardevice 101 implemented in a vehicle, e.g., vehicle 100. In other aspectsa radar device, e.g., radar device 101, may be implemented as part ofany other element of a traffic system or network, for example, as partof a road infrastructure, and/or any other element of a traffic networkor system. Other aspects may be implemented with respect to any othersystem, environment and/or apparatus, which may be implemented in anyother object, environment, location, or place. For example, radar device101 may be part of a non-vehicular device, which may be implemented, forexample, in an indoor location, a stationary infrastructure outdoors, orany other location.

In some demonstrative aspects, radar device 101 may be configured tosupport security usage. In one example, radar device 101 may beconfigured to determine a nature of an operation, e.g., a human entry,an animal entry, an environmental movement, and the like, to identity athreat level of a detected event, and/or any other additional oralternative operations.

Some demonstrative aspects may be implemented with respect to any otheradditional or alternative devices and/or systems, for example, for arobot, e.g., as described below.

In other aspects, radar device 101 may be configured to support anyother usages and/or applications.

Reference is now made to FIG. 2 , which schematically illustrates ablock diagram of a robot 200 implementing a radar, in accordance withsome demonstrative aspects.

In some demonstrative aspects, robot 200 may include a robot arm 201.The robot 200 may be implemented, for example, in a factory for handlingan object 213, which may be, for example, a part that should be affixedto a product that is being manufactured. The robot arm 201 may include aplurality of movable members, for example, movable members 202, 203,204, and a support 205. Moving the movable members 202, 203, and/or 204of the robot arm 201, e.g., by actuation of associated motors, may allowphysical interaction with the environment to carry out a task, e.g.,handling the object 213.

In some demonstrative aspects, the robot arm 201 may include a pluralityof joint elements, e.g., joint elements 207, 208, 209, which mayconnect, for example, the members 202, 203, and/or 204 with each other,and with the support 205. For example, a joint element 207, 208, 209 mayhave one or more joints, each of which may provide rotatable motion,e.g., rotational motion, and/or translatory motion, e.g., displacement,to associated members and/or motion of members relative to each other.The movement of the members 202, 203, 204 may be initiated by suitableactuators.

In some demonstrative aspects, the member furthest from the support 205,e.g., member 204, may also be referred to as the end-effector 204 andmay include one or more tools, such as, a claw for gripping an object, awelding tool, or the like. Other members, e.g., members 202, 203, closerto the support 205, may be utilized to change the position of theend-effector 204, e.g., in three-dimensional space. For example, therobot arm 201 may be configured to function similarly to a human arm,e.g., possibly with a tool at its end.

In some demonstrative aspects, robot 200 may include a (robot)controller 206 configured to implement interaction with the environment,e.g., by controlling the robot arm's actuators, according to a controlprogram, for example, in order to control the robot arm 201 according tothe task to be performed.

In some demonstrative aspects, an actuator may include a componentadapted to affect a mechanism or process in response to being driven.The actuator can respond to commands given by the controller 206 (theso-called activation) by performing mechanical movement. This means thatan actuator, typically a motor (or electromechanical converter), may beconfigured to convert electrical energy into mechanical energy when itis activated (i.e. actuated).

In some demonstrative aspects, controller 206 may be in communicationwith a radar processor 210 of the robot 200.

In some demonstrative aspects, a radar fronted 211 and a radar antennaarrangement 212 may be coupled to the radar processor 210. In oneexample, radar fronted 211 and/or radar antenna arrangement 212 may beincluded, for example, as part of the robot arm 201.

In some demonstrative aspects, the radar frontend 211, the radar antennaarrangement 212 and the radar processor 210 may be operable as, and/ormay be configured to form, a radar device. For example, antennaarrangement 212 may be configured to perform one or more functionalitiesof antenna arrangement 102 (FIG. 1 ), radar frontend 211 may beconfigured to perform one or more functionalities of radar frontend 103(FIG. 1 ), and/or radar processor 210 may be configured to perform oneor more functionalities of radar processor 104 (FIG. 1 ), e.g., asdescribed above.

In some demonstrative aspects, for example, the radar frontend 211 andthe antenna arrangement 212 may be controlled, e.g., by radar processor210, to transmit a radio transmit signal 214.

In some demonstrative aspects, as shown in FIG. 2 , the radio transmitsignal 214 may be reflected by the object 213, resulting in an echo 215.

In some demonstrative aspects, the echo 215 may be received, e.g., viaantenna arrangement 212 and radar frontend 211, and radar processor 210may generate radar information, for example, by calculating informationabout position, speed (Doppler) and/or direction of the object 213,e.g., with respect to robot arm 201.

In some demonstrative aspects, radar processor 210 may be configured toprovide the radar information to the robot controller 206 of the robotarm 201, e.g., to control robot arm 201. For example, robot controller206 may be configured to control robot arm 201 based on the radarinformation, e.g., to grab the object 213 and/or to perform any otheroperation.

Reference is made to FIG. 3 , which schematically illustrates a radarapparatus 300, in accordance with some demonstrative aspects.

In some demonstrative aspects, radar apparatus 300 may be implemented aspart of a device or system 301, e.g., as described below.

For example, radar apparatus 300 may be implemented as part of, and/ormay configured to perform one or more operations and/or functionalitiesof, the devices or systems described above with reference to FIG. 1an/or FIG. 2 . In other aspects, radar apparatus 300 may be implementedas part of any other device or system 301.

In some demonstrative aspects, radar device 300 may include an antennaarrangement, which may include one or more transmit antennas 302 and oneor more receive antennas 303. In other aspects, any other antennaarrangement may be implemented.

In some demonstrative aspects, radar device 300 may include a radarfrontend 304, and a radar processor 309.

In some demonstrative aspects, as shown in FIG. 3 , the one or moretransmit antennas 302 may be coupled with a transmitter (or transmitterarrangement) 305 of the radar frontend 304; and/or the one or morereceive antennas 303 may be coupled with a receiver (or receiverarrangement) 306 of the radar frontend 304, e.g., as described below.

In some demonstrative aspects, transmitter 305 may include one or moreelements, for example, an oscillator, a power amplifier and/or one ormore other elements, configured to generate radio transmit signals to betransmitted by the one or more transmit antennas 302, e.g., as describedbelow.

In some demonstrative aspects, for example, radar processor 309 mayprovide digital radar transmit data values to the radar frontend 304.For example, radar frontend 304 may include a Digital-to-AnalogConverter (DAC) 307 to convert the digital radar transmit data values toan analog transmit signal. The transmitter 305 may convert the analogtransmit signal to a radio transmit signal which is to be transmitted bytransmit antennas 302.

In some demonstrative aspects, receiver 306 may include one or moreelements, for example, one or more mixers, one or more filters and/orone or more other elements, configured to process, down-convert, radiosignals received via the one or more receive antennas 303, e.g., asdescribed below.

In some demonstrative aspects, for example, receiver 306 may convert aradio receive signal received via the one or more receive antennas 303into an analog receive signal. The radar frontend 304 may include anAnalog-to-Digital (ADC) Converter 308 to generate digital radarreception data values based on the analog receive signal. For example,radar frontend 304 may provide the digital radar reception data valuesto the radar processor 309.

In some demonstrative aspects, radar processor 309 may be configured toprocess the digital radar reception data values, for example, to detectone or more objects, e.g., in an environment of the device/system 301.This detection may include, for example, the determination ofinformation including one or more of range, speed (Doppler), direction,and/or any other information, of one or more objects, e.g., with respectto the system 301.

In some demonstrative aspects, radar processor 309 may be configured toprovide the determined radar information to a system controller 310 ofdevice/system 301. For example, system controller 310 may include avehicle controller, e.g., if device/system 301 includes a vehiculardevice/system, a robot controller, e.g., if device/system 301 includes arobot device/system, or any other type of controller for any other typeof device/system 301.

In some demonstrative aspects, system controller 310 may be configuredto control one or more controlled system components 311 of the system301, e.g. a motor, a brake, steering, and the like, e.g. by one or morecorresponding actuators.

In some demonstrative aspects, radar device 300 may include a storage312 or a memory 313, e.g., to store information processed by radar 300,for example, digital radar reception data values being processed by theradar processor 309, radar information generated by radar processor 309,and/or any other data to be processed by radar processor 309.

In some demonstrative aspects, device/system 301 may include, forexample, an application processor 314 and/or a communication processor315, for example, to at least partially implement one or morefunctionalities of system controller 310 and/or to perform communicationbetween system controller 310, radar device 300, the controlled systemcomponents 311, and/or one or more additional elements of device/system301.

In some demonstrative aspects, radar device 300 may be configured togenerate and transmit the radio transmit signal in a form, which maysupport determination of range, speed, and/or direction, e.g., asdescribed below.

For example, a radio transmit signal of a radar may be configured toinclude a plurality of pulses. For example, a pulse transmission mayinclude the transmission of short high-power bursts in combination withtimes during which the radar device listens for echoes.

For example, in order to more optimally support a highly dynamicsituation, e.g., in an automotive scenario, a continuous wave (CW) mayinstead be used as the radio transmit signal. However, a continuouswave, e.g., with constant frequency, may support velocity determination,but may not allow range determination, e.g., due to the lack of a timemark that could allow distance calculation.

In some demonstrative aspects, radio transmit signal 105 (FIG. 1 ) maybe transmitted according to technologies such as, for example,Frequency-Modulated continuous wave (FMCW) radar, Phase-ModulatedContinuous Wave (PMCW) radar, Orthogonal Frequency Division Multiplexing(OFDM) radar, and/or any other type of radar technology, which maysupport determination of range, velocity, and/or direction, e.g., asdescribed below.

Reference is made to FIG. 4 , which schematically illustrates a FMCWradar apparatus, in accordance with some demonstrative aspects.

In some demonstrative aspects, FMCW radar device 400 may include a radarfrontend 401, and a radar processor 402. For example, radar frontend 304(FIG. 3 ) may include one or more elements of, and/or may perform one ormore operations and/or functionalities of, radar frontend 401; and/orradar processor 309 (FIG. 3 ) may include one or more elements of,and/or may perform one or more operations and/or functionalities of,radar processor 402.

In some demonstrative aspects, FMCW radar device 400 may be configuredto communicate radio signals according to an FMCW radar technology,e.g., rather than sending a radio transmit signal with a constantfrequency.

In some demonstrative aspects, radio frontend 401 may be configured toramp up and reset the frequency of the transmit signal, e.g.,periodically, for example, according to a saw tooth waveform 403. Inother aspects, a triangle waveform, or any other suitable waveform maybe used.

In some demonstrative aspects, for example, radar processor 402 may beconfigured to provide waveform 403 to frontend 401, for example, indigital form, e.g., as a sequence of digital values.

In some demonstrative aspects, radar frontend 401 may include a DAC 404to convert waveform 403 into analog form, and to supply it to avoltage-controlled oscillator 405. For example, oscillator 405 may beconfigured to generate an output signal, which may befrequency-modulated in accordance with the waveform 403.

In some demonstrative aspects, oscillator 405 may be configured togenerate the output signal including a radio transmit signal, which maybe fed to and sent out by one or more transmit antennas 406.

In some demonstrative aspects, the radio transmit signal generated bythe oscillator 405 may have the form of a sequence of chirps 407, whichmay be the result of the modulation of a sinusoid with the saw toothwaveform 403.

In one example, a chirp 407 may correspond to the sinusoid of theoscillator signal frequency-modulated by a “tooth” of the saw toothwaveform 403, e.g., from the minimum frequency to the maximum frequency.

In some demonstrative aspects, FMCW radar device 400 may include one ormore receive antennas 408 to receive a radio receive signal. The radioreceive signal may be based on the echo of the radio transmit signal,e.g., in addition to any noise, interference, or the like.

In some demonstrative aspects, radar frontend 401 may include a mixer409 to mix the radio transmit signal with the radio receive signal intoa mixed signal.

In some demonstrative aspects, radar frontend 401 may include a filter,e.g., a Low Pass Filter (LPF) 410, which may be configured to filter themixed signal from the mixer 409 to provide a filtered signal. Forexample, radar frontend 401 may include an ADC 411 to convert thefiltered signal into digital reception data values, which may beprovided to radar processor 402. In another example, the filter 410 maybe a digital filter, and the ADC 411 may be arranged between the mixer409 and the filter 410.

In some demonstrative aspects, radar processor 402 may be configured toprocess the digital reception data values to provide radar information,for example, including range, speed (velocity/Doppler), and/or direction(AoA) information of one or more objects.

In some demonstrative aspects, radar processor 402 may be configured toperform a first Fast Fourier Transform (FFT) (also referred to as “rangeFFT”) to extract a delay response, which may be used to extract rangeinformation, and/or a second FFT (also referred to as “Doppler FFT”) toextract a Doppler shift response, which may be used to extract velocityinformation, from the digital reception data values.

In other aspects, any other additional or alternative methods may beutilized to extract range information. In one example, in a digitalradar implementation, a correlation with the transmitted signal may beused, e.g., according to a matched filter implementation.

Reference is made to FIG. 5 , which schematically illustrates anextraction scheme, which may be implemented to extract range and speed(Doppler) estimations from digital reception radar data values, inaccordance with some demonstrative aspects. For example, radar processor104 (FIG. 1 ), radar processor 210 (FIG. 2 ), radar processor 309 (FIG.3 ), and/or radar processor 402 (FIG. 4 ), may be configured to extractrange and/or speed (Doppler) estimations from digital reception radardata values according to one or more aspects of the extraction scheme ofFIG. 5 .

In some demonstrative aspects, as shown in FIG. 5 , a radio receivesignal, e.g., including echoes of a radio transmit signal, may bereceived by a receive antenna array 501. The radio receive signal may beprocessed by a radio radar frontend 502 to generate digital receptiondata values, e.g., as described above. The radio radar frontend 502 mayprovide the digital reception data values to a radar processor 503,which may process the digital reception data values to provide radarinformation, e.g., as described above.

In some demonstrative aspects, the digital reception data values may berepresented in the form of a data cube 504. For example, the data cube504 may include digitized samples of the radio receive signal, which isbased on a radio signal transmitted from a transmit antenna and receivedby M receive antennas. In some demonstrative aspects, for example, withrespect to a MIMO implementation, there may be multiple transmitantennas, and the number of samples may be multiplied accordingly.

In some demonstrative aspects, a layer of the data cube 504, forexample, a horizontal layer of the data cube 504, may include samples ofan antenna, e.g., a respective antenna of the M antennas.

In some demonstrative aspects, data cube 504 may include samples for Kchirps. For example, as shown in FIG. 5 , the samples of the chirps maybe arranged in a so-called “slow time”-direction.

In some demonstrative aspects, the data cube 504 may include L samples,e.g., L=512 or any other number of samples, for a chirp, e.g., per eachchirp. For example, as shown in FIG. 5 , the samples per chirp may bearranged in a so-called “fast time”-direction of the data cube 504.

In some demonstrative aspects, radar processor 503 may be configured toprocess a plurality of samples, e.g., L samples collected for each chirpand for each antenna, by a first FFT. The first FFT may be performed,for example, for each chirp and each antenna, such that a result of theprocessing of the data cube 504 by the first FFT may again have threedimensions, and may have the size of the data cube 504 while includingvalues for L range bins, e.g., instead of the values for the L samplingtimes.

In some demonstrative aspects, radar processor 503 may be configured toprocess the result of the processing of the data cube 504 by the firstFFT, for example, by processing the result according to a second FFTalong the chirps, e.g., for each antenna and for each range bin.

For example, the first FFT may be in the “fast time” direction, and thesecond FFT may be in the “slow time” direction.

In some demonstrative aspects, the result of the second FFT may provide,e.g., when aggregated over the antennas, a range/Doppler (R/D) map 505.The R/D map may have FFT peaks 506, for example, including peaks of FFToutput values (in terms of absolute values) for certain range/speedcombinations, e.g., for range/Doppler bins. For example, a range/Dopplerbin may correspond to a range bin and a Doppler bin. For example, radarprocessor 503 may consider a peak as potentially corresponding to anobject, e.g., of the range and speed corresponding to the peak's rangebin and speed bin.

In some demonstrative aspects, the extraction scheme of FIG. 5 may beimplemented for an FMCW radar, e.g., FMCW radar 400 (FIG. 4 ), asdescribed above. In other aspects, the extraction scheme of FIG. 5 maybe implemented for any other radar type. In one example, the radarprocessor 503 may be configured to determine a range/Doppler map 505from digital reception data values of a PMCW radar, an OFDM radar, orany other radar technologies. For example, in adaptive or cognitiveradar, the pulses in a frame, the waveform and/or modulation may bechanged over time, e.g., according to the environment.

Referring back to FIG. 3 , in some demonstrative aspects, receiveantenna arrangement 303 may be implemented using a receive antenna arrayhaving a plurality of receive antennas (or receive antenna elements).For example, radar processor 309 may be configured to determine an angleof arrival of the received radio signal, e.g., echo 105 (FIG. 1 ) and/orecho 215 (FIG. 2 ). For example, radar processor 309 may be configuredto determine a direction of a detected object, e.g., with respect to thedevice/system 301, for example, based on the angle of arrival of thereceived radio signal, e.g., as described below.

Reference is made to FIG. 6 , which schematically illustrates anangle-determination scheme, which may be implemented to determine Angleof Arrival (AoA) information based on an incoming radio signal receivedby a receive antenna array 600, in accordance with some demonstrativeaspects.

FIG. 6 depicts an angle-determination scheme based on received signalsat the receive antenna array. In some demonstrative aspects, forexample, in a virtual MIMO array, the angle-determination may also bebased on the signals transmitted by the array of Tx antennas.

FIG. 6 depicts a one-dimensional angle-determination scheme. Othermulti-dimensional angle determination schemes, e.g., a two-dimensionalscheme or a three-dimensional scheme, may be implemented.

In some demonstrative aspects, as shown in FIG. 6 , the receive antennaarray 600 may include M antennas (numbered, from left to right, 1 to M).

As shown by the arrows in FIG. 6 , it is assumed that an echo is comingfrom an object located at the top left direction. Accordingly, thedirection of the echo, e.g., the incoming radio signal, may be towardsthe bottom right. According to this example, the further to the left areceive antenna is located, the earlier it will receive a certain phaseof the incoming radio signal.

For example, a phase difference, denoted Δφ, between two antennas of thereceive antenna array 601 may be determined, e.g., as follows:

${\Delta\varphi} = {\frac{2\pi}{\lambda} \cdot d \cdot {\sin(\theta)}}$

wherein λ denotes a wavelength of the incoming radio signal, d denotes adistance between the two antennas, and θ denotes an angle of arrival ofthe incoming radio signal, e.g., with respect to a normal direction ofthe array.

In some demonstrative aspects, radar processor 309 (FIG. 3 ) may beconfigured to utilize this relationship between phase and angle of theincoming radio signal, for example, to determine the angle of arrival ofechoes, for example by performing an FFT, e.g., a third FFT (“angularFFT”) over the antennas.

In some demonstrative aspects, multiple transmit antennas, e.g., in theform of an antenna array having multiple transmit antennas, may be used,for example, to increase the spatial resolution, e.g., to providehigh-resolution radar information. For example, a MIMO radar device mayutilize a virtual MIMO radar antenna, which may be formed as aconvolution of a plurality of transmit antennas convolved with aplurality of receive antennas.

Reference is made to FIG. 7 , which schematically illustrates a MIMOradar antenna scheme, which may be implemented based on a combination ofTransmit (Tx) and Receive (Rx) antennas, in accordance with somedemonstrative aspects.

In some demonstrative aspects, as shown in FIG. 7 , a radar MIMOarrangement may include a transmit antenna array 701 and a receiveantenna array 702. For example, the one or more transmit antennas 302(FIG. 3 ) may be implemented to include transmit antenna array 701,and/or the one or more receive antennas 303 (FIG. 3 ) may be implementedto include receive antenna array 702.

In some demonstrative aspects, antenna arrays including multipleantennas both for transmitting the radio transmit signals and forreceiving echoes of the radio transmit signals, may be utilized toprovide a plurality of virtual channels as illustrated by the dashedlines in FIG. 7 . For example, a virtual channel may be formed as aconvolution, for example, as a Kronecker product, between a transmitantenna and a receive antenna, e.g., representing a virtual steeringvector of the MIMO radar.

In some demonstrative aspects, a transmit antenna, e.g., each transmitantenna, may be configured to send out an individual radio transmitsignal, e.g., having a phase associated with the respective transmitantenna.

For example, an array of N transmit antennas and M receive antennas maybe implemented to provide a virtual MIMO array of size N×M. For example,the virtual MIMO array may be formed according to the Kronecker productoperation applied to the Tx and Rx steering vectors.

FIG. 8 is a schematic block diagram illustration of a radar frontend 804and a radar processor 834, in accordance with some demonstrativeaspects. For example, radar frontend 103 (FIG. 1 ), radar frontend 211(FIG. 1 ), radar frontend 304 (FIG. 3 ), radar frontend 401 (FIG. 4 ),and/or radar frontend 502 (FIG. 5 ), may include one or more elements ofradar frontend 804, and/or may perform one or more operations and/orfunctionalities of radar frontend 804.

In some demonstrative aspects, radar frontend 804 may be implemented aspart of a MIMO radar utilizing a MIMO radar antenna 881 including aplurality of Tx antennas 814 configured to transmit a plurality of Tx RFsignals (also referred to as “Tx radar signals”); and a plurality of Rxantennas 816 configured to receive a plurality of Rx RF signals (alsoreferred to as “Rx radar signals”), for example, based on the Tx radarsignals, e.g., as described below.

In some demonstrative aspects, MIMO antenna array 881, antennas 814,and/or antennas 816 may include or may be part of any type of antennassuitable for transmitting and/or receiving radar signals. For example,MIMO antenna array 881, antennas 814, and/or antennas 816, may beimplemented as part of any suitable configuration, structure, and/orarrangement of one or more antenna elements, components, units,assemblies, and/or arrays. For example, MIMO antenna array 881, antennas814, and/or antennas 816, may be implemented as part of a phased arrayantenna, a multiple element antenna, a set of switched beam antennas,and/or the like. In some aspects, MIMO antenna array 881, antennas 814,and/or antennas 816, may be implemented to support transmit and receivefunctionalities using separate transmit and receive antenna elements. Insome aspects, MIMO antenna array 881, antennas 814, and/or antennas 816,may be implemented to support transmit and receive functionalities usingcommon and/or integrated transmit/receive elements.

In some demonstrative aspects, MIMO radar antenna 881 may include arectangular MIMO antenna array, and/or curved array, e.g., shaped to fita vehicle design. In other aspects, any other form, shape and/orarrangement of MIMO radar antenna 881 may be implemented.

In some demonstrative aspects, radar frontend 804 may include one ormore radios configured to generate and transmit the Tx RF signals via Txantennas 814; and/or to process the Rx RF signals received via Rxantennas 816, e.g., as described below.

In some demonstrative aspects, radar frontend 804 may include at leastone transmitter (Tx) 883 including circuitry and/or logic configured togenerate and/or transmit the Tx radar signals via Tx antennas 814.

In some demonstrative aspects, radar frontend 804 may include at leastone receiver (Rx) 885 including circuitry and/or logic to receive and/orprocess the Rx radar signals received via Rx antennas 816, for example,based on the Tx radar signals.

In some demonstrative aspects, transmitter 883, and/or receiver 885 mayinclude circuitry; logic; Radio Frequency (RF) elements, circuitryand/or logic; baseband elements, circuitry and/or logic; modulationelements, circuitry and/or logic; demodulation elements, circuitryand/or logic; amplifiers; analog to digital and/or digital to analogconverters; filters; and/or the like.

In some demonstrative aspects, transmitter 883 may include a pluralityof Tx chains 810 configured to generate and transmit the Tx RF signalsvia Tx antennas 814, e.g., respectively; and/or receiver 885 may includea plurality of Rx chains 812 configured to receive and process the Rx RFsignals received via the Rx antennas 816, e.g., respectively.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 813, for example, based on the radar signalscommunicated by MIMO radar antenna 881, e.g., as described below. Forexample, radar processor 104 (FIG. 1 ), radar processor 210 (FIG. 1 ),radar processor 309 (FIG. 3 ), radar processor 402 (FIG. 4 ), and/orradar processor 503 (FIG. 5 ), may include one or more elements of radarprocessor 834, and/or may perform one or more operations and/orfunctionalities of radar processor 834.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 813, for example, based on radar Rx data 811received from the plurality of Rx chains 812. For example, radar Rx data811 may be based on the Rx RF signals received via the Rx antennas 816.

In some demonstrative aspects, radar processor 834 may include an input832 to receive the radar Rx data 811 from the plurality of Rx chains812.

In some demonstrative aspects, radar processor 834 may include at leastone processor 836, which may be configured, for example, to process theradar Rx data 811, and/or to perform one or more operations, methods,and/or algorithms.

In some demonstrative aspects, radar processor 834 may include at leastone memory 838, e.g., coupled to the processor 836. For example, memory838 may be configured to store data processed by radar processor 834.For example, memory 838 may store, e.g., at least temporarily, at leastsome of the information processed by the processor 836, and/or logic tobe utilized by the processor 836.

In some demonstrative aspects, memory 838 may be configured to store atleast part of the radar data, e.g., some of the radar Rx data or all ofthe radar Rx data, for example, for processing by processor 836, e.g.,as described below.

In some demonstrative aspects, memory 838 may be configured to storeprocessed data, which may be generated by processor 836, for example,during the process of generating the radar information 813, e.g., asdescribed below.

In some demonstrative aspects, memory 838 may be configured to storerange information and/or Doppler information, which may be generated byprocessor 836, for example, based on the radar Rx data, e.g., asdescribed below. In one example, the range information and/or Dopplerinformation may be determined based on a Cross-Correlation (XCORR)operation, which may be applied to the radar RX data. Any otheradditional or alternative operation, algorithm and/or procedure may beutilized to generate the range information and/or Doppler information.

In some demonstrative aspects, memory 838 may be configured to store AoAinformation, which maybe generated by processor 836, for example, basedon the radar Rx data, the range information and/or Doppler information,e.g., as described below. In one example, the AoA information may bedetermined based on an AoA estimation algorithm. Any other additional oralternative operation, algorithm and/or procedure may be utilized togenerate the AoA information.

In some demonstrative aspects, radar processor 834 may be configured togenerate the radar information 813 including one or more of rangeinformation, Doppler information, and/or AoA information, e.g., asdescribed below.

In some demonstrative aspects, the radar information 813 may includePoint Cloud 1 (PC1) information, for example, including raw point cloudestimations, e.g., Range, Radial Velocity, Azimuth and/or Elevation.

In some demonstrative aspects, the radar information 813 may includePoint Cloud 2 (PC2) information, which may be generated, for example,based on the PC1 information. For example, the PC2 information mayinclude clustering information, tracking information, e.g., tracking ofprobabilities and/or density functions, bounding box information,classification information, orientation information, and the like.

In some demonstrative aspects, radar processor 834 may be configured togenerate the radar information 813 in the form of four Dimensional (4D)image information, e.g., a cube, which may represent 4D informationcorresponding to one or more detected targets.

In some demonstrative aspects, the 4D image information may include, forexample, range values, e.g., based on the range information, velocityvalues, e.g., based on the Doppler information, azimuth values, e.g.,based on azimuth AoA information, elevation values, e.g., based onelevation AoA information, and/or any other values.

In some demonstrative aspects, radar processor 834 may be configured togenerate the radar information 813 in any other form, and/or includingany other additional or alternative information.

In some demonstrative aspects, radar processor 834 may be configured toprocess the signals communicated via MIMO radar antenna 881 as signalsof a virtual MIMO array formed by a convolution of the plurality of Rxantennas 816 and the plurality of Tx antennas 814.

In some demonstrative aspects, radar frontend 804 and/or radar processor834 may be configured to utilize MIMO techniques, for example, tosupport a reduced physical array aperture, e.g., an array size, and/orutilizing a reduced number of antenna elements. For example, radarfrontend 804 and/or radar processor 834 may be configured to transmitorthogonal signals via a Tx array including a plurality of N elements,e.g., Tx antennas 814, and processing received signals via an Rx arrayincluding a plurality of M elements, e.g., Rx antennas 816.

In some demonstrative aspects, utilizing the MIMO technique oftransmission of the orthogonal signals from the Tx array with N elementsand processing the received signals in the Rx array with M elements maybe equivalent, e.g., under a far field approximation, to a radarutilizing transmission from one antenna and reception with N*M antennas.For example, radar frontend 804 and/or radar processor 834 may beconfigured to utilize MIMO antenna array 881 as a virtual array havingan equivalent array size of N*M, which may define locations of virtualelements, for example, as a convolution of locations of physicalelements, e.g., the antennas 814 and/or 816.

In some demonstrative aspects, there may be a need to provide atechnical solution to efficiently and/or accurately calibrate an antennaarray. For example, antennas may be manufactured with some enhancedphase and gain as their ground state, and, accordingly, an antenna arraymay be dysfunctional, for example, if the antenna elements of the arrayare not calibrated.

In some demonstrative aspects, calibrating an antenna array by placing atarget at known location and with the expected gain and phase upon eachantenna may have one or more technical inefficiencies, disadvantagesand/or problems in one or more use cases and/or scenarios. For example,a single target may not be enough to calibrate the antennas, since anantenna pattern is non-isotropic. Accordingly, multiple antennas atvarious locations may be required to calibrate the whole antenna array.Such a solution may be expensive in terms of time and cost.

Some demonstrative aspects may be configured to provide a technicalsolution to support calibrating an antenna array at multiple instances,for example, at one or more defined instances, in a dynamic manner,and/or in real-time, for example, post installation and/or duringoperation of radar frontend 804. In one example, radar processor 834 maybe configured to support calibration of MIMO antenna array 881, forexample, upon or after installation of one or more antenna elements ofMIMO antenna array 881, and/or at one or more later times, for example,after treatment of one or more antenna elements in antenna array 881and/or after treatment to surroundings of the MIMO antenna array 881. Inone example, if MIMO antenna array 881 is placed behind a car bumper,then calibration may be performed based on installment, treatment and/orchange in the car bumper, e.g., after an accident, for example, sincethe car bumper may add some mismatch to the calibration of the MIMOantenna array 881.

In some demonstrative aspects, radar processor 834 may be configured toperform a mismatch calibration to calibrate a mismatch of MIMO antennaarray 881, for example, to improve performance of the antenna array 881,e.g., as described below.

In some demonstrative aspects, radar processor 834 may be configured toperform the mismatch calibration, for example, to provide a technicalsolution, which may even avoid factory calibrations and/or use ofspecial hardware.

In some demonstrative aspects, there may be a need to provide atechnical solution to efficiently and/or accurately calibrate an antennaarray mismatch (MM) of an antenna array, for example, by calibrating atleast one of a Gain MM (GMM), a Phase MM (PMM), and/or a Cross Coupling(CC) between elements of the antenna array.

In some demonstrative aspects, there may be a need to provide atechnical solution to achieve a sufficient Peak Side Lobe Level (PSLL)of an estimated AoA spectrum, which may be a Key Performance Indicator(KPI) for radar systems.

In some demonstrative aspects, the PSLL of an AoA spectrum may bedetermined as a difference between a power level (e.g., in decibel (dB))of a main-lobe in the AoA spectrum, and a power level (e.g., in dB) of apeak, e.g., maximal, side-lobe corresponding to the main-lobe. Forexample, according to this PSLL determination, a higher PSLL may beconsidered better than a lower PSLL.

In some demonstrative aspects, for example, when the main-lobe has apower level of 0 dB, then the PSLL may be determined as a positive valueaccording to the power level of the peak side-lobe corresponding to themain-lobe.

In other aspects, the PSLL of an AoA spectrum may be determined as adifference between a power level (e.g., in dB) of a peak, e.g., maximal,side-lobe corresponding to a main-lobe, and a power level (e.g., in dB)of the main-lobe in the AoA spectrum. For example, according to thisPSLL definition, the PSLL may include a negative value. For example,according to this PSLL definition, a lower PSLL may be considered betterthan a higher PSLL.

In some demonstrative aspects, the PSLL may be affected by the antennaarray mismatch. For example, in order to achieve a sufficient, e.g.,improved, PSLL level, there may be a need to reduce an antenna mismatch,for example, a reduced GMM, e.g., less than ˜0.05 dB in gain variation,and/or a reduced PMM, e.g., less than ˜0.25 deg in angle variation.

In one example, a first PSLL level of 60 dB, and a second PSLL level of45 dB, may be achieved, for example, based on the following mismatchlevels:

TABLE 1 Required PSLL TX GMM TX PMM RX GMM RX PMM [dB] [dB] [Deg] [dB][Deg] 60 0.05 0.25 0.05 0.25 45 0.3 3 0.3 3

For example, according to Table 1, there may be a need to reduce themismatch parameters, e.g., even less than ˜0.5 dB, for example, in orderto achieve improved PSLL levels of at least 30 dB, e.g., 45 dB or 60 dB.

In another example, any other combination of MM parameters may beimplemented.

In some demonstrative aspects, implementing an antenna array withoutantenna MM calibration, may result in PSLL levels which may result inone or more technical inefficiencies, disadvantages and/or problems inone or more use cases and/or scenarios, e.g., as described below.

Reference is made to FIG. 9 , which schematically illustrates adetection scenario 900, and a graph 910 depicting a plurality of AoAspectrum images corresponding to the radar detection scenario 900, todemonstrate a technical problem, which may be addressed in accordancewith some demonstrative aspects.

A shown in FIG. 9 , detection scenario 900 shows a human 912 crossing aroad or just standing on the road, and a parked car 914, which may bedetected by a vehicular radar 915.

According to the detection scenario 900, the human 912 and the car 914may be detected by the vehicular radar 915 as being in a sameRange-Doppler (RD) bin, e.g., the human 912 and the car 914 may bedetected by the vehicular radar 915 to be at about the same range andvelocity.

For example, according to the detection scenario 900, there may be aneed to be able to differentiate between the human 912 and the car 914,for example, by detecting a different AoA for the human 912 and the car914, for example, as estimated by vehicular radar 915.

In some demonstrative aspects, as shown in FIG. 9 , an AoA spectrumimage 902 may be based on Rx radar signals received by a MIMO antennaarray which does not have antenna mismatch.

In some demonstrative aspects, as shown in FIG. 9 , an AoA spectrumimage 904 may be based on Rx radar signals received by a MIMO antennaarray having an antenna mismatch, and without performing any antennamismatch calibration.

In some demonstrative aspects, as shown in FIG. 9 , the antenna mismatchmay result in a “corrupted” AoA spectrum image 904, having an increasednoise level, for example, e.g., compared to the AoA image 902 withoutmismatch.

In some demonstrative aspects, as shown in FIG. 9 , an AoA spectrumimage 906 may be determined based on the Rx radar signals received bythe MIMO antenna array with the antenna mismatch, when applying anantenna MM calibration, e.g., as described below.

In some demonstrative aspects, as shown in FIG. 9 , the AoA spectrumimage 906 of the MIMO antenna array with the antenna mismatchcalibration may be relatively similar to the AoA spectrum image 902 ofthe antenna array without the antenna mismatch.

In some demonstrative aspects, according to the example of FIG. 9 , asshown by the AoA spectrum image 902, a Radar Cross Section (RCS) 216corresponding to the car 914 may be about 40 dB higher than an RCS 913corresponding to human 912. According to this example, there may be aneed to achieve a PSLL level of about 55 dB with respect to the RCS ofthe car 916, for example, in order to allow detecting the human RCS 913,e.g., assuming a Post Processing Signal To Noise Ratio SNR (PPSNR) fordetection is about ˜15 dB.

For example, as shown in FIG. 9 , the AoA spectrum image 904 may have aPSLL level of less than 20 dB, e.g., as may be determined based on thedifference between the power level of the main-lobe, e.g., about 28 dB,and the power level of the peak side-lobe corresponding to themain-lobe, e.g., about 10 dB.

In some demonstrative aspects, as shown in FIG. 9 , the AoA spectrumimage 904 may suffer from an increased noise level, which may not allowto differentiate between the RCS 913 corresponding to human 912 and theRCS 916 corresponding to the car 914.

In some demonstrative aspects, as shown in FIG. 9 , the AoA spectrumimage 906 may have a PSLL level of about 58 dB, e.g., as may bedetermined based on the difference between the power level of themain-lobe, e.g., about 28 dB, and the power level of the peak side-lobecorresponding to the main-lobe, e.g., about −30 dB.

In some demonstrative aspects, as shown in FIG. 9 , the AoA spectrumimage 906 may allow to differentiate between the RCS 913 correspondingto human 912, and the RCS 916 corresponding to the car 914.

For example, the PSLL of about 58 dB of AoA spectrum image 906 may besufficient to allow differentiating between the AoA of the human 912 andthe AoA of the car 914.

Referring back to FIG. 8 , in some demonstrative aspects, radarprocessor 834 may be configured to generate radar information 813, forexample, based on radar Rx data 811 and a mismatch calibration, e.g., asdescribed below. For example, radar processor 834 may receive the radarRx data 811 vie input 832, for example, from the Rx chains 812. Forexample, radar Rx data 811 may be based on radar signals received via Rxantennas 816.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 833 including AoA information, for example,azimuth AoA information, elevation AoA information, and/or any otherAoA-based information.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 833 including an AoA spectrum, e.g., anazimuth AoA spectrum an elevation AoA spectrum, and/or any otherAoA-based spectrum.

In some demonstrative aspects, radar processor 834 may be configured toperform a mismatch calibration to calibrate a mismatch of MIMO antennaarray 881, for example, to improve performance of the antenna array 881,for example, to achieve a PSLL of at least 30 dB, e.g., as describedbelow.

In some demonstrative aspects, radar processor 834 may be configured toperform the mismatch calibration, for example, to provide a technicalsolution, which may allow to calibrate a mismatch of antenna array 881,for example, while utilizing a computationally efficient calibrationtechnique, and/or in a manner which may support a reduced calibrationtime, e.g., as described below.

In some demonstrative aspects, radar processor 834 may be configured toperform the mismatch calibration of MIMO antenna array 881, for example,according to a calibration technique, which may be performed within arelatively short time period, e.g., even within a few minutes.

In some demonstrative aspects, radar processor 834 may be configured toperform the mismatch calibration of MIMO antenna array 881, for example,according to a calibration technique, which may be performed inreal-time, and/or according to a suitable timing scheme, for example, todynamically calibrate the mismatch calibration of MIMO antenna array881.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 813 based on the radar Rx data 811, forexample, by calibrating an antenna Mismatch (MM) of the MIMO radarantenna 881, for example, such that the radar information 813 includesan AoA spectrum having a PSLL of at least 30 dB, e.g., as describedbelow.

For example, the PSLL may be defined and/or determined as a differencebetween a power level of a main-lobe of the AoA spectrum and a powerlevel of a peak side-lobe corresponding to the main-lobe in the AoAspectrum, e.g., as described below.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 813 including the AoA spectrum having a PSLLof at least 35 dB, for example, wherein the PSLL is determined as adifference between a power level of a main-lobe of the AoA spectrum anda power level of a peak side-lobe corresponding to the main-lobe in theAoA spectrum, e.g., as described below.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 813 including the AoA spectrum having a PSLLof at least 40 dB, for example, wherein the PSLL is determined as adifference between a power level of a main-lobe of the AoA spectrum anda power level of a peak side-lobe corresponding to the main-lobe in theAoA spectrum, e.g., as described below.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 813 including the AoA spectrum having a PSLLof at least 45 dB, for example, wherein the PSLL is determined as adifference between a power level of a main-lobe of the AoA spectrum anda power level of a peak side-lobe corresponding to the main-lobe in theAoA spectrum, e.g., as described below.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 813 including the AoA spectrum having a PSLLof at least 50 dB, for example, wherein the PSLL is determined as adifference between a power level of a main-lobe of the AoA spectrum anda power level of a peak side-lobe corresponding to the main-lobe in theAoA spectrum, e.g., as described below.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 813 including the AoA spectrum having a PSLLof at least 55 dB, for example, wherein the PSLL is determined as adifference between a power level of a main-lobe of the AoA spectrum anda power level of a peak side-lobe corresponding to the main-lobe in theAoA spectrum, e.g., as described below.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 113 including the AoA spectrum having a PSLLof at least 60 dB, for example, wherein the PSLL is determined as adifference between a power level of a main-lobe of the AoA spectrum anda power level of a peak side-lobe corresponding to the main-lobe in theAoA spectrum, e.g., as described below.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 813 including the AoA spectrum having anyother suitable PSLL level.

In some demonstrative aspects, memory 838 may be configured to store afirst calibration matrix and a second calibration matrix, e.g., asdescribed below.

In some demonstrative aspects, as shown in FIG. 8 , memory 838 may beimplemented as part of radar processor 834. In other aspects, memory 838may be implemented as a dedicated memory and/or as part of any otherelement of radar frontend 804, and/or any other device or systemimplementing radar frontend 804 and/or radar processor 834, e.g.,vehicle 100 (FIG. 1 ).

In some demonstrative aspects, radar processor 836 may be configured toretrieve the first calibration matrix and the second calibration matrixfrom the memory 838, and to generate the radar information 813 bycalibrating the antenna MM of the MIMO radar antenna 881 based on thefirst calibration matrix and the second calibration matrix, e.g., asdescribed below.

In some demonstrative aspects, the first calibration matrix may includean Rx calibration matrix, and/or the second calibration matrix mayinclude a Tx calibration matrix, e.g., as described below.

In some demonstrative aspects, the Rx calibration matrix may correspondto an Rx MM of the plurality of Rx antennas 816; and/or the Txcalibration matrix may correspond to a Tx MM of the plurality of Txantennas 814, of the MIMO radar antenna 881, e.g., as described below.

In some demonstrative aspects, the first calibration matrix may have asize of M*M, and/or the second calibration matrix may have a size ofN*N, wherein M denotes a count of the Rx antennas 816, and N denotes acount of the Tx antennas 814, e.g., as described below.

In some demonstrative aspects, radar processor 834 may be configured toupdate at least one of the first calibration matrix and/or the secondcalibration matrix, for example, based on a change in at least oneparameter affecting the antenna MM of the MIMO radar antenna 881, e.g.,as described below.

In some demonstrative aspects, radar processor 834 may be configured todynamically update, e.g., in real-time or with shot latency, at leastone of the first calibration matrix and/or the second calibrationmatrix, for example, based on a detected change in at least oneparameter affecting the antenna MM of the MIMO radar antenna, e.g., asdescribed below.

In some demonstrative aspects, radar processor 834 may be configured todynamically update, e.g., in real-time or with shot latency, at leastone of the first calibration matrix and/or the second calibrationmatrix, and to perform the mismatch calibration of MIMO antenna array881 based on the updated first calibration matrix and/or secondcalibration matrix, e.g., as described below.

In some demonstrative aspects, radar processor 834 may be configured todynamically update, e.g., in real-time or with shot latency, at leastone of the first calibration matrix and/or the second calibrationmatrix, and/or perform the mismatch calibration of MIMO antenna array881, for example, at one or more time points.

In some demonstrative aspects, radar processor 834 may be configured toautomatically and/or autonomously update, e.g., in real-time or withshot latency, at least one of the first calibration matrix and/or thesecond calibration matrix, and/or perform the mismatch calibration ofMIMO antenna array 881, for example, according to a timing scheme, forexample, once a day, once a week, once a month, and/or at any other timeinterval.

In some demonstrative aspects, radar processor 834 may be configured todynamically update, e.g., in real-time or with shot latency, at leastone of the first calibration matrix and/or the second calibrationmatrix, and/or perform the mismatch calibration of MIMO antenna array881, for example, based on one or more events and/or criteria.

In on example, radar processor 834 may be configured to dynamicallyupdate, e.g., in real-time or with shot latency, at least one of thefirst calibration matrix and/or the second calibration matrix, and/orperform the mismatch calibration of MIMO antenna array 881, for example,based on installation and/or treatment of one or more elements of MIMOantenna array 881, e.g., after installation of antenna array 881, e.g.,on vehicle 100 (FIG. 1 ), and/or upon a treatment, replacement, and/oradjustment of one or more elements of MIMO antenna array 881.

In on example, radar processor 834 may be configured to dynamicallyupdate, e.g., in real-time or with shot latency, at least one of thefirst calibration matrix and/or the second calibration matrix, and/orperform the mismatch calibration of MIMO antenna array 881, for example,based on installation and/or treatment of one or more other elements,which may have an effect on antenna array 881, for example, a newcalibration of the antenna array, e.g., Tx antennas 814 and Rx antennas816, after treatment to vehicle 100 (FIG. 1 ), for example, switching ofa bumper of vehicle 100 (FIG. 1 ), and/or any other treatment,maintenance, or service.

In some demonstrative aspects, configuring radar processor 834 toperform the mismatch calibration of MIMO antenna array 881, e.g., asdescribed herein, may provide a technical solution which may mitigate,reduce, and/or even avoid, errors in the radar information, which may becaused by changes in one or more elements of radar frontend 804 and/orvehicle 100 (FIG. 1 ), e.g., including aging, temperature changes, caraccidents, radar movements and/or installation, and the like.

In some demonstrative aspects, radar processor 836 may be configured togenerate intermediate radar information 873 based on the Rx radarsignals, e.g., as described below.

In some demonstrative aspects, for example, the intermediate radarinformation 873 may include intermediate range-Doppler information basedon the radar Rx data 811.

In some demonstrative aspects, radar processor 836 may be configured togenerate the intermediate range-Doppler information by processing theradar Rx data 811, for example, according to a range-Doppler processingscheme.

For example, radar processor 836 may be configured to generateintermediate range information by applying range-based processing, e.g.,cross correlation processing, to the radar Rx data 811.

For example, radar processor 836 may be configured to generate XCORRinformation based on the radar Rx data 811, and to generate theintermediate radar data 873, for example, by applying a Fast FourierTransform (FFT) to the XCORR information.

For example, radar processor 836 may be configured to generate theintermediate range-Doppler information by applying Doppler-basedprocessing, e.g., FFT processing, to the intermediate range information.

In other aspects, intermediate radar information 873 may include anyother information, for example, partially processed radar informationbased on radar Rx data 811.

In some demonstrative aspects, radar processor 834 may include anantenna MM calibrator 871 configured to generate calibrated intermediateradar information 875 by applying an antenna MM calibration to theintermediate radar data 873, e.g., as described below.

In some demonstrative aspects, antenna mismatch calibrator 871 may beimplemented as part of radar processor 834. In other aspects, antennamismatch calibrator 832 and radar processor 834 may be implemented asseparate and/or dedicated elements, e.g., processors, of radar frontend804, and/or any other device or system implementing radar frontend 804and/or radar processor 834, e.g., vehicle 100 (FIG. 1 ).

In some demonstrative aspects, radar processor 834 may be configured togenerate the radar information 813 based on the calibrated intermediateradar information 875, e.g., as described below.

In some demonstrative aspects, radar processor 834 may be configured togenerate the radar information 813 by applying to the calibratedintermediate radar information 875 one or more radar-processingoperations, for example, AoA-based processing operations, and/or anyother additional or alternative radar processing operations.

In some demonstrative aspects, antenna MM calibrator 871 may beconfigured to retrieve the first calibration matrix and/or the secondcalibration matrix from the memory 838, and to generate the calibratedintermediate radar information 875, for example, by applying the firstcalibration matrix and the second calibration matrix to the intermediateradar information 873, e.g., as described below.

In some demonstrative aspects, antenna MM calibrator 871 may beconfigured to determine a product of the first calibration matrixmultiplied by the intermediate radar information 873, e.g., as describedbelow.

In some demonstrative aspects, antenna MM calibrator 871 may beconfigured to determine the calibrated intermediate radar information875, for example, by multiplying the product of the first calibrationmatrix and the intermediate radar information 873, for example, by thesecond calibration matrix, e.g., as described below.

In some demonstrative aspects, radar processor 836 and/or antenna MMcalibrator 871 may be configured to calibrate the antenna MM of MIMOradar antenna 881 by implementing a MM calibration technique, e.g., asdescribed below.

In some demonstrative aspects, the MM calibration technique may beconfigured to calibrate the antenna MM of MIMO radar antenna 881, forexample, based on the first and second antenna calibration matrices,e.g., as described below.

In some demonstrative aspects, the MM calibration technique may beconfigured to calibrate the antenna MM of MIMO radar antenna 881, forexample, based on the first and second antenna calibration matrices,which may be estimated based on measurements in a reduced number ofcalibration angels, for example, compared to other calibration methods.

In some demonstrative aspects, the MM calibration technique may beconfigured to calibrate the antenna MM of MIMO radar antenna 881, forexample, using a number of calibration angels, which is based on anorder of the number of Tx antennas 814, and/or an order of the number ofRx antennas 816, e.g., as described below.

In some demonstrative aspects, the MM calibration technique may beconfigured to calibrate the antenna MM of MIMO radar antenna 881, forexample, based on the first and second antenna calibration matrices,which may be estimated using a number of calibration angels, which is inan order of twice the number of Tx antennas 814 and/or the number of Rxantennas 816, e.g., as described below.

In some demonstrative aspects, the MM calibration technique may beconfigured to calibrate the antenna MM of MIMO radar antenna 881, forexample, based on the first and second antenna calibration matrices,which may be estimated using less than 500 calibration angels, forexample, with respect to a MIMO radar antenna 881 including a total ofup to about 200 antenna elements.

In some demonstrative aspects, the MM calibration technique may beconfigured to calibrate the antenna MM of MIMO radar antenna 881, forexample, based on the first and second antenna calibration matrices,which may be estimated using less than 150 calibration angels, forexample, with respect to a MIMO radar antenna 881 including 16 Txantennas 814 and 96 Rx antennas 816, which correspond to 1536 virtualelements of a virtual MIMO array.

In some demonstrative aspects, radar processor 836 and/or antenna MMcalibrator 871 may be configured to calibrate the antenna MM of MIMOradar antenna 881, for example, by implementing the MM calibrationtechnique using an algorithm, e.g., a computationally efficientalgorithm, for example, an algorithm based on Alternating least Square(ALS), while using a reduced number of calibration angles, which maysignificantly reduce a production calibration time. For example, themismatch calibration technique may use less than 150 calibration anglesfor the calibration of 1536 virtual elements, e.g., 16×96 virtualelements.

In other aspects, any other number of calibration angles, Tx antennas814, and/or Rx antennas 816, may be utilized.

In some demonstrative aspects, a radar signal model may be defined torepresent radar signals communicated between N Tx antennas, e.g., Txantennas 114, and M Rx antennas, e.g., Rx antennas 116, as follows:

X=Q _(r) a _(r)(θ)β(θ)a _(t) ^(T)(θ)Q _(t) ^(T) S+W  (1)

wherein S denotes transmitted orthogonal signals, for example, thesignals transmitted by Tx antennas 114, e.g., S ϵN×L; X denotes observedmeasurements, for example, received signals, for example, the receivedsignals at Rx antennas 116, e.g., X ϵM×L; a_(t)(θ) denotes a Tx steeringvector corresponding to the N Tx antennas, e.g., a_(t)(θ)ϵN×1; β(θ)denotes a complex amplitude, e.g., β(θ)ϵ1×1, for example, due to atarget RCS, range and/or angle; a_(r)(θ) denotes an Rx steering vectorcorresponding to the M receive antennas, e.g., a_(r)(θ)ϵM×1; W denotesan Additive white Gaussian Noise (AWGN) affecting the transmission fromthe N Tx antennas to the M Rx antennas, e.g., WϵM×L: W_(ij)˜CN(0,σ_(W)²); Q_(t) denotes a Tx array MM matrix corresponding to the N Txantennas, e.g., Q_(t)ϵN×N; and Q_(r) denotes an Rx array MM matrixcorresponding to the M Rx antennas, e.g., Q_(r)ϵM×M.

In some demonstrative aspects, the Tx steering vector a_(t)(θ) and theTx array MM matrix Q_(t) may be combined and/or rewritten, and/or the Rxsteering vector a_(r)(θ) and the Rx array MM matrix Q_(r) may becombined and/or rewritten, e.g., as follows:

Rx: ã _(r)(θ)=Q _(r) a _(r)(θ) where Q _(r) ϵM×M is the Rx MM matrix

Tx: ã _(t)(θ)=Q _(t) a _(t)(θ) where Q _(t) ϵN×N is the Tx MM matrix

$\begin{matrix}{{Q_{r} = \begin{bmatrix}q_{r_{11}} & q_{r_{12}} & q_{r_{13}} & \ldots & q_{r_{1M}} \\q_{r_{21}} & q_{r_{22}} & q_{r_{23}} & \ldots & q_{r_{2M}} \\ \vdots & & \ddots & & \\ \vdots & & & \ddots & \\q_{r_{M1}} & q_{r_{M2}} & q_{r_{M3}} & \ldots & q_{r_{MM}}\end{bmatrix}}{Q_{t} = \begin{bmatrix}q_{t_{11}} & q_{t_{12}} & q_{t_{13}} & \ldots & q_{t_{1N}} \\q_{t_{21}} & q_{t_{22}} & q_{t_{23}} & \ldots & q_{t_{2N}} \\ \vdots & & \ddots & & \\ \vdots & & & \ddots & \\q_{t_{N1}} & q_{t_{N2}} & q_{t_{N3}} & \ldots & q_{t_{NN}}\end{bmatrix}}{q_{ij} = \left\{ {{\begin{matrix}{{i = j},{GainPhaseMM}} \\{{i \neq j},{CrossCoupling}}\end{matrix}q_{ij}} = {{\left( {1 + \rho} \right)e^{j\phi}{where}:\rho} \sim {{N\left( {0,\sigma_{\rho}^{2}} \right)}\phi} \sim {N\left( {0,\sigma_{\phi}^{2}} \right)}}} \right.}} & (2)\end{matrix}$

In some demonstrative aspects, radar processor 834 and/or antenna MMcalibrator 871 may be configured to determine calibrated intermediateradar information 875, for example, by applying to intermediate radarinformation 873 a first mismatch calibration matrix, for example, an RxMM calibration matrix, denoted {circumflex over (Q)}_(r) ⁻¹, and asecond mismatch calibration matrix, for example, a Tx MM calibrationmatrix, denoted {circumflex over (Q)}_(t) ⁻¹, e.g., as described below.

In some demonstrative aspects, the Rx MM calibration matrix {circumflexover (Q)}_(r) ⁻¹ may include an inverse of an estimated Rx MM matrix,denoted {circumflex over (Q)}_(t), which may be determined as anestimate of the Rx array MM matrix Q_(t), e.g., as described below.

In some demonstrative aspects, the Tx MM calibration matrix {circumflexover (Q)}_(t) ⁻¹ may include an inverse of an estimated Tx MM matrix,denoted {circumflex over (Q)}_(t), which may be determined as anestimate of the Tx array MM matrix Q_(t), e.g., as described below.

In some demonstrative aspects, the estimated Rx MM matrix {circumflexover (Q)}_(r) and the estimated Tx MM matrix {circumflex over (Q)}_(t)may be estimated separately, e.g., as described below. This separateestimation may allow an efficient estimation, e.g., in terms ofcomputational effort, calibration time and/or calibration accuracy. Inother aspects, any other procedure may be implemented to estimate theestimated Rx MM matrix {circumflex over (Q)}_(r), and/or the estimatedTx MM matrix {circumflex over (Q)}_(t), separately or in combination.

In some demonstrative aspects, radar processor 834 and/or antenna MMcalibrator 871 may be configured to determine calibrated intermediateradar information, denoted Y_(corrected), e.g., calibrated intermediateradar information 875, for example, by applying the calibration matrices{circumflex over (Q)}_(r) ⁻¹ and {circumflex over (Q)}_(t) ⁻¹ tointermediate radar information, denoted Y_(corrupted), e.g.,intermediate radar information 873, as follows:

Y _(corrected) ={circumflex over (Q)} _(r) ⁻¹ Y _(corrupted) {circumflexover (Q)} _(t) ⁻¹  (3)

In some demonstrative aspects, the Rx MM calibration matrix {circumflexover (Q)}_(r) ⁻¹ and/or the Tx MM calibration matrix {circumflex over(Q)}_(t) ⁻¹ may be estimated according to an estimation procedure, forexample utilizing a plurality of calibration images, e.g., which may bemeasured at a plurality of calibration angles, e.g., as described below.

In some demonstrative aspects, the Rx MM calibration matrix {circumflexover (Q)}_(r) ⁻¹ and/or the Tx MM calibration matrix {circumflex over(Q)}_(r) ⁻¹ may be preconfigured and stored in memory 838, e.g., duringa production and/or calibration procedure of radar frontend 804.

In some demonstrative aspects, radar processor 834 and/or antenna MMcalibrator 871 may be configured to determine the Rx MM calibrationmatrix {circumflex over (Q)}_(r) ⁻¹ and/or the Tx MM calibration matrix{circumflex over (Q)}_(t) ⁻¹.

In some demonstrative aspects, radar processor 834 and/or antenna MMcalibrator 871 may be configured to determine and/or update the Rx MMcalibration matrix {circumflex over (Q)}_(r) ⁻¹ and/or the Tx MMcalibration matrix {circumflex over (Q)}_(t) ⁻¹, for example, based onchanges in one or more parameters, which may affect the antenna MM ofMIMO radar antenna 881.

In one example, the changes in the one or more parameters, which mayaffect the antenna MM of MIMO radar antenna 881 may include, forexample, changes in a configuration and/arrangement of one or moreantenna elements of MIMO radar antenna 881, and/or changes in one ormore elements of a vehicle, e.g., vehicle 100 (FIG. 1 ) on which theMIMO radar antenna 881 is installed, which may affect the functionalityof MIMO radar antenna 881.

In some demonstrative aspects, radar processor 834 and/or antenna MMcalibrator 871 may be configured to determine and/or update the Rx MMcalibration matrix {circumflex over (Q)}_(r) ⁻¹ and/or the Tx MMcalibration matrix {circumflex over (Q)}_(t) ⁻¹, for example, postproduction of radar frontend 804, and/or post installment of radarfrontend 804 on vehicle 100 (FIG. 1 ).

In some demonstrative aspects, radar processor 834 and/or antenna MMcalibrator 871 may be configured to dynamically update the Rx MMcalibration matrix {circumflex over (Q)}_(r) ⁻¹ and/or the Tx MMcalibration matrix {circumflex over (Q)}_(t) ⁻¹, for example, inreal-time, e.g., based on detected changes in one or more parameters,which may affect the antenna MM of MIMO radar antenna 881.

In some demonstrative aspects, the estimated Rx MM matrix {circumflexover (Q)}_(r) and/or the Tx MM calibration matrix {circumflex over(Q)}_(t) may be determined, for example, based on an estimationprocedure including one or more of the following operations:

-   -   Estimate radar information, denoted Y_(k), e.g., range-Doppler        information, from K calibration angles, e.g. as follows:

Y _(k) =Q _(r) a _(r)(θ_(k))a _(t) ^(T)(θ_(k))Q _(t) ^(T) +{tilde over(W)} _(K) =Q _(r) A(θ_(k))Q _(t) ^(T) +{tilde over (W)} _(K)  (4)

-   -   Determine the estimated Rx MM matrix and the Rx MM calibration        matrix {circumflex over (Q)}_(t) based on the radar information        Y_(k), for example, using an ALS algorithm, e.g., as follows:        -   Initialize Q_(r)=I        -   Solve for Q_(t):

${{1.\begin{bmatrix}Y_{1} \\ \vdots \\Y_{k}\end{bmatrix}} = {{{\begin{bmatrix}Q_{r} & & \\ & \ddots & \\ & & Q_{r}\end{bmatrix}\begin{bmatrix}A_{1} \\ \vdots \\A_{k}\end{bmatrix}}Q_{t}^{T}} + \begin{bmatrix}{\overset{\sim}{W}}_{1} \\ \vdots \\{\overset{\sim}{W}}_{K}\end{bmatrix}}}{{2.\overset{\sim}{Y}} = {{{{\overset{\sim}{Q}}_{r}\overset{\sim}{A}Q_{t}^{T}} + \overset{\sim}{W}} = {\overset{\sim}{Y} = {{HQ}_{t}^{T} + \overset{\sim}{W}}}}}{{3.{\hat{Q}}_{t}} = \left\lbrack {\left( {H^{H}H} \right)^{- 1}H^{H}\overset{\sim}{Y}} \right\rbrack^{T}}$

-   -   -   Solve for Q_(r):

${{1.\left\lbrack {Y_{1}\ldots Y_{k}} \right\rbrack} = {{{Q_{r}\left\lbrack {A_{1}\ldots A_{k}} \right\rbrack}\begin{bmatrix}Q_{t}^{T} & & \\ & \ddots & \\ & & Q_{t}^{T}\end{bmatrix}} + \left\lbrack {{\overset{\sim}{W}}_{1}\ldots{\overset{\sim}{W}}_{K}} \right\rbrack}}{{2.\overset{\sim}{Y}} = {{{{\overset{\sim}{Q}}_{r}\overset{\sim}{A}\overset{\sim}{Q_{t}^{T}}} + \overset{\sim}{W}} = {\overset{\sim}{Y} = {{Q_{r}H} + \overset{\sim}{W}}}}}{{3.{\hat{Q}}_{r}} = {\overset{\sim}{Y}{H^{H}\left( {HH}^{H} \right)}^{- 1}}}$

-   -   -   Repeat solving for Q_(t) and for Q_(r) until convergence.

In some demonstrative aspects, a cost function may be defined based on aNormalized Root Mean Square Error (NRMSE), e.g., as follows:

$\begin{matrix}{{Cost} = {{NRMSE} = {\frac{{{{\hat{Q}}_{r} - Q_{r}}}_{F}}{{Q_{r}}_{F}} + \frac{{{{\hat{Q}}_{t} - Q_{t}}}_{F}}{{Q_{t}}_{F}}}}} & (5)\end{matrix}$

In some demonstrative aspects, a stopping Criteria for an n-th iterationof the estimation procedure may be defined, for example, based on thecost function, e.g., as follows:

$\begin{matrix}{{\frac{{{{\hat{Q}}_{r,n} - {\hat{Q}}_{r,{n - 1}}}}_{F}}{{{\hat{Q}}_{r,{n - 1}}}_{F}} + \frac{{{{\hat{Q}}_{t,n} - {\hat{Q}}_{t,{n - 1}}}}_{F}}{{{\hat{Q}}_{t,{n - 1}}}_{F}}} < 10^{- 5}} & (6)\end{matrix}$

In other aspects, any other cost function and/or stopping criteria maybe utilized.

In other aspects, any other additional or alternative operations and/orprocedures may be implemented to determine the estimated Rx MM matrix{circumflex over (Q)}_(r) and/or the Tx MM calibration matrix{circumflex over (Q)}_(r).

Reference is made to FIG. 10 , which schematically illustratesperformance graphs of radar processing with antenna mismatchcalibration, in accordance with some demonstrative aspects.

In one example, the performance graphs of FIG. 10 may correspond to aMIMO radar antenna including 24 Tx antenna elements, and 64 Rx antennaelements, with respect to a target located at an azimuth of about 40degrees and at an elevation of about 70 degrees.

As shown in FIG. 10 , the target may be clearly identified on atwo-dimensional (2D) azimuth-elevation map 1010, which may be generatedbased on measurements of Rx radar signals received by a MIMO antennaarray not having an antenna MM.

As shown in FIG. 10 , the target may not be identified on a 2Dazimuth-elevation map 1012, which may be generated based on measurementsof Rx radar signals received by a MIMO antenna array having an antennaMM, for example, if antenna MM calibration is not applied.

As shown in FIG. 10 , the target may be clearly identified on a 2Dazimuth-elevation map 1014, which may be generated based on measurementsof Rx radar signals received by a MIMO antenna array having an antennaMM, for example, if antenna MM calibration is applied, e.g., accordingto the antenna MM calibration described above.

In some demonstrative aspects, as shown in FIG. 10 , the 2Dazimuth-elevation map 1014 generated utilizing the antenna MMcalibration may provide similar results to the 2D azimuth-elevation map1010 when there is no antenna MM.

In some demonstrative aspects, as shown in FIG. 10 , a cost function1016 may converge, for example, after less than 30 iterations of the ALSestimation method.

In some demonstrative aspects, as shown in FIG. 10 , an azimuth AoAimage 1026, which may be generated based on the Rx radar signalsreceived by the MIMO antenna array with the antenna MM calibration, mayprovide similar results compared to an azimuth AoA image 1022, which maybe based on the Rx radar signals received by the MIMO antenna array nothaving an antenna MM.

In some demonstrative aspects, as shown in FIG. 10 , an elevation AoAimage 1036, which may be generated based on the Rx radar signalsreceived by the MIMO antenna array with the antenna MM calibration, mayprovide similar results compared to an elevation AoA image 1032, whichmay be based on the Rx radar signals received by the MIMO antenna arraynot having an antenna MM.

In some demonstrative aspects, as shown in FIG. 10 , elevation AoA image1026 and azimuth AoA image 1016 show that the antenna MM calibration mayachieve a PSLL, which is better, for example, than 57 dB.

In some demonstrative aspects, calibrating an antenna array usingboresight calibration may have one or more technical inefficiencies,disadvantages and/or problems in one or more use cases and/or scenarios.For example, while the boresight calibration may be implemented tocalibrate only at one angle, which may be relatively simple, theboresight calibration may have a reduced Side Lobe Level (SLL)performance, e.g., of about ˜15 dB.

In some demonstrative aspects, calibrating an antenna array using anempirical Maximum Likelihood (ML) based correction may have one or moretechnical inefficiencies, disadvantages and/or problems in one or moreuse cases and/or scenarios. For example, the empirical ML basedcorrection may calibrate an antenna array, for example, by scanning anentire 2D AoA grid, which may be greater than a number of virtualelements, constructing an empirical steering matrix, e.g., an arraymanifold, and projecting the steering matrix on the received signal,e.g., after Range-Doppler (RD) processing. For example, while ML-basedcorrection method may be suitable for maximizing an SNR, this method maynot be suitable for minimizing the SLL, and accordingly, may not besuitable for achieving a suitable PSLL, e.g., a high PSLL.

Reference is made to FIG. 11A, which schematically illustrates a graph1110 depicting a signal to Noise Ratio (SNR) performance of radarprocessing with antenna mismatch calibration, and to FIG. 11B, whichschematically illustrates a graph 1120 depicting a PSLL performance ofradar processing with antenna mismatch calibration, in accordance withsome demonstrative aspects.

In some demonstrative aspects, as shown in FIG. 1A, a curve 1102represents an SNR based on Rx radar signals received by a MIMO antennaarray with antenna MM calibration, e.g., as described above.

In some demonstrative aspects, as shown in FIG. 11A, the SNR performancecurve 1102 of the MIMO antenna array with the antenna MM calibration,may be comparable to an SNR performance of other calibration algorithms.

In some demonstrative aspects, as shown in FIG. 11A, a curve 1106represents a PSLL, which may be achieved based on processing the Rxradar signals received by the MIMO antenna array with the antenna MMcalibration, e.g., as described above.

In some demonstrative aspects, as shown in FIG. 11B, the PSLLperformance achieved by the antenna MM calibration may be comparable toa PSLL performance 1108 of an antenna array not having an antenna MM.

In some demonstrative aspects, as shown in FIGS. 11A and 11B, theantenna MM calibration may achieve a significantly improved PSLL, forexample, while maintaining a suitable SNR, e.g., compared to othercalibration algorithms.

Reference is made to FIG. 12A, which schematically illustrates a graph1210 depicting an SNR loss performance of radar processing with antennamismatch calibration, and to FIG. 12B, which schematically illustrates agraph 1220 depicting PSLL performance of radar processing with antennamismatch calibration, in accordance with some demonstrative aspects.

In some demonstrative aspects, as shown in FIG. 12A, a curve 1212represents an SNR loss of radar processing based on Rx radar signalsreceived by a MIMO antenna array while applying the antenna MMcalibration, e.g., as described above.

In some demonstrative aspects, as shown in FIG. 12B, a curve 1222represents a PSLL achieved by radar processing based on Rx radar signalsreceived by the MIMO antenna array while applying the antenna MMcalibration, e.g., as described above.

In some demonstrative aspects, as shown in FIG. 12A, when a Tx GMMstandard deviation is below 2 dB, which is the expected native GMM, theSNR loss of curve 1212 may be negligible, e.g., less than 1 dB.

In some demonstrative aspects, as shown in FIG. 12B, the PSLLperformance of curve 1222 may be comparable to the PSLL performance of atheoretical array not having antenna MM, for example, while maintainingthe SNR loss negligible, e.g., as shown in FIG. 12A.

Reference is made to FIG. 13 , which schematically illustrates graphs1310, 1320 and 1330 depicting antenna gain mismatches, which may be usedas a reference in accordance with some demonstrative aspects.

For example, the graphs 1310, 1320 and 1330 represent native Tx and RxGMM, e.g., without any tuning.

As shown in FIG. 13 , both Tx GMM and Rx GMM may have a standarddeviation of less than 2 dB. Accordingly, the antenna MM calibration,e.g., as described above, may be implemented to improved PSLLperformance, e.g., as shown by curve 1222 (FIG. 12B), for example, whilemaintaining very low SNR degradation, e.g., less than 1 dB, as shown bycurve 1212 (FIG. 12A).

Reference is made to FIG. 14 , which schematically illustrates a methodof radar antenna calibration, in accordance with some demonstrativeaspects. For example, one or more of the operations of the method ofFIG. 14 may be performed by one or more elements of a system, forexample, one or more vehicles, e.g., vehicle 100 (FIG. 1 ), a radardevice, e.g., radar device 101 (FIG. 1 ), a mismatch calibrator, e.g.,mismatch calibrator 871 (FIG. 8 ), a radar processor, e.g., radarprocessor 836 (FIG. 8 ) and/or radar processor 834 (FIG. 8 ).

As indicated at block 1402, the method may include processing radar Rxdata, which may be based on radar signals received via a plurality of Rxantennas of a MIMO radar antenna. For example, radar processor 834 (FIG.8 ) may process the radar Rx data 811 (FIG. 8 ), e.g., as describedabove.

As indicated at block 1404, the method may include generating radarinformation based on the radar Rx data by calibrating an antenna MM ofthe MIMO radar antenna such that the radar information includes an AoAspectrum having a PSLL of at least 30 dB, for example, wherein the PSLLis determined as a difference between a power level of a main-lobe ofthe AoA spectrum and a power level of a peak side-lobe corresponding tothe main-lobe in the AoA spectrum. For example, antenna MM calibrator871 (FIG. 8 ) may calibrate the antenna MM of the MIMO radar antenna 881(FIG. 8 ), e.g., as described above.

Reference is made to FIG. 15 , which schematically illustrates a productof manufacture 1500, in accordance with some demonstrative aspects.Product 1500 may include one or more tangible computer-readable(“machine-readable”) non-transitory storage media 1502, which mayinclude computer-executable instructions, e.g., implemented by logic1504, operable to, when executed by at least one computer processor,enable the at least one computer processor to implement one or moreoperations at a vehicle, e.g., vehicle 100 (FIG. 1 ), a radar device,e.g., radar device 101 (FIG. 1 ), a mismatch calibrator, e.g., mismatchcalibrator 871 (FIG. 8 ), a radar processor, e.g., radar processor 836(FIG. 8 ) and/or radar processor 834 (FIG. 8 ); to cause a vehicle,e.g., vehicle 100 (FIG. 1 ), a radar device, e.g., radar device 101(FIG. 1), a mismatch calibrator, e.g., mismatch calibrator 871 (FIG. 8), a radar processor, e.g., radar processor 836 (FIG. 8 ) and/or radarprocessor 834 (FIG. 8 ), to perform, trigger and/or implement one ormore operations and/or functionalities; and/or to perform, triggerand/or implement one or more operations and/or functionalities describedwith reference to the FIGS. 1-14 , and/or one or more operationsdescribed herein. The phrases “non-transitory machine-readable medium”and “computer-readable non-transitory storage media” may be directed toinclude all machine and/or computer readable media, with the soleexception being a transitory propagating signal.

In some demonstrative aspects, product 1500 and/or storage media 1502may include one or more types of computer-readable storage media capableof storing data, including volatile memory, non-volatile memory,removable or non-removable memory, erasable or non-erasable memory,writeable or re-writeable memory, and the like. For example, storagemedia 1502 may include, RAM, DRAM, Double-Data-Rate DRAM (DDR-DRAM),SDRAM, static RAM (SRAM), ROM, programmable ROM (PROM), erasableprogrammable ROM (EPROM), electrically erasable programmable ROM(EEPROM), Compact Disk ROM (CD-ROM), Compact Disk Recordable (CD-R),Compact Disk Rewriteable (CD-RW), flash memory (e.g., NOR or NAND flashmemory), content addressable memory (CAM), polymer memory, phase-changememory, ferroelectric memory, silicon-oxide-nitride-oxide-silicon(SONOS) memory, a disk, a floppy disk, a hard drive, an optical disk, amagnetic disk, a card, a magnetic card, an optical card, a tape, acassette, and the like. The computer-readable storage media may includeany suitable media involved with downloading or transferring a computerprogram from a remote computer to a requesting computer carried by datasignals embodied in a carrier wave or other propagation medium through acommunication link, e.g., a modem, radio or network connection.

In some demonstrative aspects, logic 1504 may include instructions,data, and/or code, which, if executed by a machine, may cause themachine to perform a method, process, and/or operations as describedherein. The machine may include, for example, any suitable processingplatform, computing platform, computing device, processing device,computing system, processing system, computer, processor, or the like,and may be implemented using any suitable combination of hardware,software, firmware, and the like.

In some demonstrative aspects, logic 1504 may include, or may beimplemented as, software, a software module, an application, a program,a subroutine, instructions, an instruction set, computing code, words,values, symbols, and the like. The instructions may include any suitabletype of code, such as source code, compiled code, interpreted code,executable code, static code, dynamic code, and the like. Theinstructions may be implemented according to a predefined computerlanguage, manner, or syntax, for instructing a processor to perform acertain function. The instructions may be implemented using any suitablehigh-level, low-level, object-oriented, visual, compiled and/orinterpreted programming language, such as C, C++, Java, BASIC, Matlab,Pascal, Visual BASIC, assembly language, machine code, and the like.

EXAMPLES

The following examples pertain to further aspects.

Example 1 includes an apparatus comprising an input to receive radarreceive (Rx) data, the radar Rx data based on radar signals received viaa plurality of Rx antennas of a Multiple-Input-Multiple-Output (MIMO)radar antenna; and a radar processor configured to generate radarinformation based on the radar Rx data by calibrating an antennaMismatch (MM) of the MIMO radar antenna such that the radar informationincludes an Angle of Arrival (AoA) spectrum having a Peak Side LobeLevel (PSLL) of at least 30 decibel (dB), wherein the PSLL is determinedas a difference between a power level of a main-lobe of the AoA spectrumand a power level of a peak side-lobe corresponding to the main-lobe inthe AoA spectrum.

Example 2 includes the subject matter of Example 1, and optionally,comprising a memory to store a first calibration matrix and a secondcalibration matrix, the radar processor configured to retrieve the firstcalibration matrix and the second calibration matrix from the memory,and to generate the radar information by calibrating the antenna MM ofthe MIMO radar antenna based on the first calibration matrix and thesecond calibration matrix.

Example 3 includes the subject matter of Example 2, and optionally,wherein the first calibration matrix comprises an Rx calibration matrix,and the second calibration matrix comprises a Transmit (Tx) calibrationmatrix, the Rx calibration matrix corresponding to an Rx MM of theplurality of Rx antennas, the Tx calibration matrix corresponding to aTx MM of a plurality of Tx antennas of the MIMO radar antenna.

Example 4 includes the subject matter of Example 2 or 3, and optionally,wherein a size of the first calibration matrix is M*M, and a size of thesecond calibration matrix is N*N, wherein M denotes a count of the Rxantennas, and N denotes a count of the Tx antennas.

Example 5 includes the subject matter of any one of Examples 2-4, andoptionally, wherein the radar processor is configured to update at leastone of the first calibration matrix or the second calibration matrixbased on a change in at least one parameter to affect the antenna MM ofthe MIMO radar antenna.

Example 6 includes the subject matter of any one of Examples 2-5, andoptionally, wherein the radar processor is configured to, e.g.,dynamically, update at least one of the first calibration matrix or thesecond calibration matrix, e.g., in real-time, based on a detectedchange in at least one parameter to affect the antenna MM of the MIMOradar antenna.

Example 7 includes the subject matter of any one of Examples 1-6, andoptionally, wherein the radar processor is configured to generateintermediate radar data based on the radar Rx data, the radar processorcomprising an antenna MM calibrator configured to generate calibratedintermediate radar information by applying an antenna MM calibration tothe intermediate radar data, the radar processor configured to generatethe radar information based on the calibrated intermediate radarinformation.

Example 8 includes the subject matter of Example 7, and optionally,wherein the intermediate radar information comprises intermediaterange-Doppler information based on the radar Rx data.

Example 9 includes the subject matter of Example 7 or 8, and optionally,wherein the radar processor is configured to generate Cross-Correlation(XCORR) information based on the radar Rx data, and to generate theintermediate radar data by applying a Fast Fourier Transform (FFT) tothe XCORR information.

Example 10 includes the subject matter of any one of Examples 7-9, andoptionally, comprising a memory to store a first calibration matrix anda second calibration matrix, the antenna MM calibrator configured toretrieve the first calibration matrix and the second calibration matrixfrom the memory, and to generate the calibrated intermediate radarinformation by applying the first calibration matrix and the secondcalibration matrix to the intermediate radar information.

Example 11 includes the subject matter of Example 10, and optionally,wherein the antenna MM calibrator is configured to determine a productof the first calibration matrix multiplied by the intermediate radarinformation, and to determine the calibrated intermediate radarinformation by multiplying by the second calibration matrix the productof the first calibration matrix and the intermediate radar information.

Example 12 includes the subject matter of Example 10 or 11, andoptionally, wherein the first calibration matrix comprises an Rxcalibration matrix, and the second calibration matrix comprises aTransmit (Tx) calibration matrix, the Rx calibration matrixcorresponding to an Rx MM of the plurality of Rx antennas, the Txcalibration matrix corresponding to a Tx MM of a plurality of Txantennas of the MIMO radar antenna.

Example 13 includes the subject matter of any one of Examples 10-12, andoptionally, wherein a size of the first calibration matrix is M*M, and asize of the second calibration matrix is N*N, wherein M denotes a countof the Rx antennas, and N denotes a count of the Tx antennas.

Example 14 includes the subject matter of any one of Examples 1-13, andoptionally, wherein the radar processor is configured to generate theradar information by calibrating the antenna MM of the MIMO radarantenna to provide the radar information including the AoA spectrumhaving a PSLL of at least 40 dB.

Example 15 includes the subject matter of any one of Examples 1-14, andoptionally, wherein the radar processor is configured to generate theradar information by calibrating the antenna MM of the MIMO radarantenna to provide the radar information including the AoA spectrumhaving a PSLL of at least 50 dB.

Example 16 includes the subject matter of any one of Examples 1-15, andoptionally, wherein the radar processor is configured to generate theradar information by calibrating the antenna MM of the MIMO radarantenna to provide the radar information including the AoA spectrumhaving a PSLL of at least 55 dB.

Example 17 includes the subject matter of any one of Examples 1-16, andoptionally, wherein the radar processor is configured to generate theradar information by calibrating the antenna MM of the MIMO radarantenna to provide the radar information including the AoA spectrumhaving a PSLL of at least 60 dB.

Example 18 includes the subject matter of any one of Examples 1-17, andoptionally, comprising the MIMO radar antenna, a plurality of Transmit(Tx) chains to transmit a plurality of Tx signals via a plurality of Txantennas of the MIMO radar antenna, and a plurality of Rx chains togenerate the radar Rx data based on the radar signals received via theplurality of Rx antennas.

Example 19 includes the subject matter of any one of Examples 1-18, andoptionally, comprising a vehicle, the vehicle comprising a systemcontroller configured to control one or more vehicular systems of thevehicle based on the radar information.

Example 20 includes an apparatus comprising means for executing any ofthe described operations of Examples 1-19.

Example 21 includes a machine-readable medium that stores instructionsfor execution by a processor to perform any of the described operationsof Examples 1-19.

Example 22 includes an apparatus comprising a memory; and processingcircuitry configured to perform any of the described operations ofExamples 1-19.

Example 23 includes a method including any of the described operationsof Examples 1-19.

Functions, operations, components and/or features described herein withreference to one or more aspects, may be combined with, or may beutilized in combination with, one or more other functions, operations,components and/or features described herein with reference to one ormore other aspects, or vice versa.

While certain features have been illustrated and described herein, manymodifications, substitutions, changes, and equivalents may occur tothose skilled in the art. It is, therefore, to be understood that theappended claims are intended to cover all such modifications and changesas fall within the true spirit of the disclosure.

1.-24. (canceled)
 25. An apparatus comprising: an input to receive radarreceive (Rx) data, the radar Rx data based on radar signals received viaa plurality of Rx antennas of a Multiple-Input-Multiple-Output (MIMO)radar antenna; and a radar processor configured to generate radarinformation based on the radar Rx data by calibrating an antennaMismatch (MM) of the MIMO radar antenna such that the radar informationincludes an Angle of Arrival (AoA) spectrum having a Peak Side LobeLevel (PSLL) of at least 30 decibel (dB), wherein the PSLL is determinedas a difference between a power level of a main-lobe of the AoA spectrumand a power level of a peak side-lobe corresponding to the main-lobe inthe AoA spectrum.
 26. The apparatus of claim 25 comprising a memory tostore a first calibration matrix and a second calibration matrix, theradar processor configured to retrieve the first calibration matrix andthe second calibration matrix from the memory, and to generate the radarinformation by calibrating the antenna MM of the MIMO radar antennabased on the first calibration matrix and the second calibration matrix.27. The apparatus of claim 26, wherein the first calibration matrixcomprises an Rx calibration matrix, and the second calibration matrixcomprises a Transmit (Tx) calibration matrix, the Rx calibration matrixcorresponding to an Rx MM of the plurality of Rx antennas, the Txcalibration matrix corresponding to a Tx MM of a plurality of Txantennas of the MIMO radar antenna.
 28. The apparatus of claim 26,wherein a size of the first calibration matrix is M*M, and a size of thesecond calibration matrix is N*N, wherein M denotes a count of the Rxantennas, and N denotes a count of the Tx antennas.
 29. The apparatus ofclaim 26, wherein the radar processor is configured to update at leastone of the first calibration matrix or the second calibration matrixbased on a change in at least one parameter to affect the antenna MM ofthe MIMO radar antenna.
 30. The apparatus of claim 26, wherein the radarprocessor is configured to update, in real time, at least one of thefirst calibration matrix or the second calibration matrix based on adetected change in at least one parameter to affect the antenna MM ofthe MIMO radar antenna.
 31. The apparatus of claim 25, wherein the radarprocessor is configured to generate intermediate radar data based on theradar Rx data, the radar processor comprising an antenna MM calibratorconfigured to generate calibrated intermediate radar information byapplying an antenna MM calibration to the intermediate radar data, theradar processor configured to generate the radar information based onthe calibrated intermediate radar information.
 32. The apparatus ofclaim 31, wherein the intermediate radar information comprisesintermediate range-Doppler information based on the radar Rx data. 33.The apparatus of claim 31, wherein the radar processor is configured togenerate Cross-Correlation (XCORR) information based on the radar Rxdata, and to generate the intermediate radar data by applying a FastFourier Transform (FFT) to the XCORR information.
 34. The apparatus ofclaim 31 comprising a memory to store a first calibration matrix and asecond calibration matrix, the antenna MM calibrator configured toretrieve the first calibration matrix and the second calibration matrixfrom the memory, and to generate the calibrated intermediate radarinformation by applying the first calibration matrix and the secondcalibration matrix to the intermediate radar information.
 35. Theapparatus of claim 34, wherein the antenna MM calibrator is configuredto determine a product of the first calibration matrix multiplied by theintermediate radar information, and to determine the calibratedintermediate radar information by multiplying by the second calibrationmatrix the product of the first calibration matrix and the intermediateradar information.
 36. The apparatus of claim 34, wherein the firstcalibration matrix comprises an Rx calibration matrix, and the secondcalibration matrix comprises a Transmit (Tx) calibration matrix, the Rxcalibration matrix corresponding to an Rx MM of the plurality of Rxantennas, the Tx calibration matrix corresponding to a Tx MM of aplurality of Tx antennas of the MIMO radar antenna.
 37. The apparatus ofclaim 34, wherein a size of the first calibration matrix is M*M, and asize of the second calibration matrix is N*N, wherein M denotes a countof the Rx antennas, and N denotes a count of the Tx antennas.
 38. Theapparatus of claim 25, wherein the radar processor is configured togenerate the radar information by calibrating the antenna MM of the MIMOradar antenna to provide the radar information including the AoAspectrum having a PSLL of at least 40 dB.
 39. The apparatus of claim 25,wherein the radar processor is configured to generate the radarinformation by calibrating the antenna MM of the MIMO radar antenna toprovide the radar information including the AoA spectrum having a PSLLof at least 55 dB.
 40. The apparatus of claim 25, wherein the radarprocessor is configured to generate the radar information by calibratingthe antenna MM of the MIMO radar antenna to provide the radarinformation including the AoA spectrum having a PSLL of at least 60 dB.41. The apparatus of claim 25 comprising the MIMO radar antenna, aplurality of Transmit (Tx) chains to transmit a plurality of Tx signalsvia a plurality of Tx antennas of the MIMO radar antenna, and aplurality of Rx chains to generate the radar Rx data based on the radarsignals received via the plurality of Rx antennas.
 42. A productcomprising one or more tangible computer-readable non-transitory storagemedia comprising instructions operable to, when executed by at least oneprocessor, enable the at least one processor to cause a radar device to:process radar receive (Rx) data, the radar Rx data based on radarsignals received via a plurality of Rx antennas of aMultiple-Input-Multiple-Output (MIMO) radar antenna; and generate radarinformation based on the radar Rx data by calibrating an antennaMismatch (MM) of the MIMO radar antenna such that the radar informationincludes an Angle of Arrival (AoA) spectrum having a Peak Side LobeLevel (PSLL) of at least 30 decibel (dB), wherein the PSLL is determinedas a difference between a power level of a main-lobe of the AoA spectrumand a power level of a peak side-lobe corresponding to the main-lobe inthe AoA spectrum.
 43. The product of claim 42, wherein the instructions,when executed, cause the radar device to store in a memory a firstcalibration matrix and a second calibration matrix, to retrieve thefirst calibration matrix and the second calibration matrix from thememory, and to generate the radar information by calibrating the antennaMM of the MIMO radar antenna based on the first calibration matrix andthe second calibration matrix.
 44. The product of claim 43, wherein thefirst calibration matrix comprises an Rx calibration matrix, and thesecond calibration matrix comprises a Transmit (Tx) calibration matrix,the Rx calibration matrix corresponding to an Rx MM of the plurality ofRx antennas, the Tx calibration matrix corresponding to a Tx MM of aplurality of Tx antennas of the MIMO radar antenna.
 45. The product ofclaim 42, wherein the instructions, when executed, cause the radardevice to generate intermediate radar data based on the radar Rx data,to generate calibrated intermediate radar information by applying anantenna MM calibration to the intermediate radar data, and to generatethe radar information based on the calibrated intermediate radarinformation.
 46. A vehicle comprising: a system controller configured tocontrol one or more vehicular systems of the vehicle based on radarinformation; and a radar device configured to provide the radarinformation to the system controller, the radar device comprising: aMultiple-Input-Multiple-Output (MIMO) radar antenna comprising aplurality of Transmit (Tx) antennas to transmit Tx radar signals, and aplurality of Receive (Rx) antennas to receive Rx radar signals based onthe Tx radar signals; and a radar processor configured to generate theradar information based on radar Rx data, the radar Rx data based on theRx radar signals, wherein the radar processor is configured to generatethe radar information by calibrating an antenna Mismatch (MM) of theMIMO radar antenna such that the radar information includes an Angle ofArrival (AoA) spectrum having a Peak Side Lobe Level (PSLL) of at least30 decibel (dB), wherein the PSLL is determined as a difference betweena power level of a main-lobe of the AoA spectrum and a power level of apeak side-lobe corresponding to the main-lobe in the AoA spectrum. 47.The vehicle of claim 46 comprising a memory to store a first calibrationmatrix and a second calibration matrix, the radar processor configuredto retrieve the first calibration matrix and the second calibrationmatrix from the memory, and to generate the radar information bycalibrating the antenna MM of the MIMO radar antenna based on the firstcalibration matrix and the second calibration matrix.
 48. The vehicle ofclaim 47, wherein the first calibration matrix comprises an Rxcalibration matrix, and the second calibration matrix comprises a Txcalibration matrix, the Rx calibration matrix corresponding to an Rx MMof the plurality of Rx antennas, the Tx calibration matrix correspondingto a Tx MM of the plurality of Tx antennas.